From f08d2f0ec43b762b5b04af89dcc03ca992eed7bc Mon Sep 17 00:00:00 2001
From: Daphne Cornelisse <33460159+daphne-cornelisse@users.noreply.github.com>
Date: Mon, 28 Apr 2025 09:47:16 -0400
Subject: [PATCH 01/38] Refactor: Unify guidance mechanisms (#421)

* Update settings

* Add warning to catch unnormalized features

* Clean done logic.

* Restructure expert suggestions under _guidance_

* Update settings

* Add warning to catch unnormalized features

* Clean done logic.

* Restructure expert suggestions under _guidance_

* Update WOSAC eval readme

* Provide agents with full speed guidance trajs

* Add support for headings

* Add vel_xy

* Improve code efficiency

* Small shape bug fix

* Improve wosac eval setting

* Update best 1-scene policy

* Refactor gym env with unified guidance mode.

* Update visualizer with unified plotting of reference traj

* Minor

* Transform reference headings to local coordinate frame.

* Give value network a bit more capacity

* Give value network a bit more capacity

* Add dataframe code

* Minor

* Add optional video logging to wosac script

* Update init mode

* Visualizer bug fix

* Minor

* wosac eval updates and new cpts
---
 baselines/ppo/config/ppo_base_puffer.yaml     |   2 +-
 ...waypoint.yaml => ppo_guided_autonomy.yaml} |  59 +-
 baselines/ppo/config/ppo_population.yaml      |   4 +-
 ...ppo_waypoint.py => ppo_guided_autonomy.py} |  20 +-
 ..._replay__S_1__04_26_09_02_20_677_000833.pt | Bin 0 -> 1353210 bytes
 ..._replay__S_3__04_27_13_13_33_780_013762.pt | Bin 0 -> 1295482 bytes
 ...oint_rs__S_1__04_23_19_37_26_618_003500.pt | Bin 1078490 -> 0 bytes
 examples/eval/README.md                       |  10 +-
 examples/eval/run_wosac_eval.py               | 194 +++--
 examples/eval/wosac_eval_origin.py            | 231 +++++-
 examples/experimental/notebooks/debug.ipynb   |   4 +-
 gpudrive/datatypes/trajectory.py              |  32 +-
 gpudrive/env/base_env.py                      |   2 +-
 gpudrive/env/config.py                        |  44 +-
 gpudrive/env/constants.py                     |   7 +-
 gpudrive/env/env_puffer.py                    |  63 +-
 gpudrive/env/env_torch.py                     | 668 +++++++++---------
 gpudrive/integrations/puffer/ppo.py           |   6 +
 gpudrive/networks/agents.py                   |  65 +-
 gpudrive/networks/late_fusion.py              |   2 +-
 gpudrive/utils/generate_sbatch.py             |  29 +-
 gpudrive/visualize/core.py                    | 326 ++++-----
 22 files changed, 1024 insertions(+), 744 deletions(-)
 rename baselines/ppo/config/{ppo_waypoint.yaml => ppo_guided_autonomy.yaml} (68%)
 rename baselines/ppo/{ppo_waypoint.py => ppo_guided_autonomy.py} (94%)
 create mode 100644 checkpoints/model_guidance_log_replay__S_1__04_26_09_02_20_677_000833.pt
 create mode 100644 checkpoints/model_guidance_log_replay__S_3__04_27_13_13_33_780_013762.pt
 delete mode 100644 checkpoints/model_waypoint_rs__S_1__04_23_19_37_26_618_003500.pt

diff --git a/baselines/ppo/config/ppo_base_puffer.yaml b/baselines/ppo/config/ppo_base_puffer.yaml
index 84cff17f2..b7d633955 100644
--- a/baselines/ppo/config/ppo_base_puffer.yaml
+++ b/baselines/ppo/config/ppo_base_puffer.yaml
@@ -15,7 +15,7 @@ environment: # Overrides default environment configs (see pygpudrive/env/config.
   road_map_obs: true
   partner_obs: true
   norm_obs: true
-  add_reference_path: false
+  add_reference_pos_xy: false
   remove_non_vehicles: false # If false, all agents are included (vehicles, pedestrians, cyclists)
   lidar_obs: false # NOTE: Setting this to true currently turns of the other observation types
   reward_type: "weighted_combination" # Options: "weighted_combination", "reward_conditioned"
diff --git a/baselines/ppo/config/ppo_waypoint.yaml b/baselines/ppo/config/ppo_guided_autonomy.yaml
similarity index 68%
rename from baselines/ppo/config/ppo_waypoint.yaml
rename to baselines/ppo/config/ppo_guided_autonomy.yaml
index 24b95d655..e3395d25d 100644
--- a/baselines/ppo/config/ppo_waypoint.yaml
+++ b/baselines/ppo/config/ppo_guided_autonomy.yaml
@@ -2,59 +2,64 @@ mode: "train"
 use_rnn: false
 eval_model_path: null
 baseline: false
-data_dir: data/processed/wosac/validation_json_1
+data_dir: data/processed/wosac/validation_json_100
 continue_training: false
 model_cpt: null
 
 environment: # Overrides default environment configs (see pygpudrive/env/config.py)
   name: "gpudrive"
   num_worlds: 100 # Number of parallel environments
-  k_unique_scenes: 1 # Number of unique scenes to sample from
+  k_unique_scenes: 100 # Number of unique scenes to sample from
   max_controlled_agents: 64 # Maximum number of agents controlled by the model. Make sure this aligns with the variable kMaxAgentCount in src/consts.hpp
   ego_state: true
   road_map_obs: true
   partner_obs: true
   norm_obs: true
+  add_previous_action: true
+
+  # Guidance through expert suggestions
+  guidance: true # If true, the agent will be guided by expert suggestions
+  guidance_mode: "log_replay" # Options: "log_replay", "vbd_amortized", "vbd_online"
+  add_reference_pos_xy: true # If true, a reference path is added to the ego observation
+  add_reference_speed: true # If true, the reference speeds are added to the ego observation
+  add_reference_heading: true # If true, the reference heading are added to the ego observation
+  prob_reference_dropout: 0.0 # Value between 0 and 1, probability of a reference point to be zeroed out
+
+  # Reward function
+  reward_type: "guided_autonomy"
+  collision_weight: -0.2
+  off_road_weight: -0.2
+  guidance_pos_xy_weight: 0.01
+  guidance_speed_weight: 0.01
+  guidance_heading_weight: 0.01
+  smoothness_weight: 0.001
+
+  init_mode: womd_tracks_to_predict
+  dynamics_model: "classic"
   remove_non_vehicles: false
   collision_behavior: "ignore"
   goal_behavior: "ignore"
-  reward_type: "follow_waypoints"
-  waypoint_distance_scale: 0.01
-  speed_distance_scale: 0.01
-  jerk_smoothness_scale: 0.001
-
-  init_mode: all_non_trivial #womd_tracks_to_predict
-  dynamics_model: "classic"
   polyline_reduction_threshold: 0.1 # Rate at which to sample points from the polyline (0 is use all closest points, 1 maximum sparsity), needs to be balanced with kMaxAgentMapObservationsCount
   sampling_seed: 42 # If given, the set of scenes to sample from will be deterministic, if None, the set of scenes will be random
   obs_radius: 50.0 # Visibility radius of the agents
   action_space_steer_disc: 15
   action_space_accel_disc: 11
+  max_steer_angle: -1.57  # pi/2 = 1.57, pi/3 = 1.05
+  max_accel_value: 4.0
   init_steps: 0 # Warmup steps
   goal_achieved_weight: 0.0
   collision_weight: -0.2
   off_road_weight: -0.2
 
-  # Versatile Behavior Diffusion (VBD)
-  use_vbd: false
-  init_steps: 0
-  vbd_trajectory_weight: 0.1 # Importance of distance to the vbd trajectories in the reward function
-  vbd_in_obs: false
-
-  # Planning guidance
-  add_reference_path: true # If true, a reference path is added to the ego observation
-  add_reference_speed: true # If true, the reference speed (scalar) is added to the ego observation
-  prob_reference_dropout: 0.0 # Value between 0 and 1, probability of a reference point to be zeroed out
-
 wandb:
   entity: ""
   project: "humanlike"
-  group: "debug"
+  group: "wosac_scale_100_base"
   mode: "online" # Options: online, offline, disabled
   tags: ["ppo", "ff"]
 
 train:
-  exp_id: waypoint_rs # Set dynamically in the script if needed
+  exp_id: guidance_log_replay # Set dynamically in the script if needed
   seed: 42
   cpu_offload: false
   device: "cuda" # Dynamically set to cuda if available, else cpu
@@ -66,7 +71,7 @@ train:
   resample_scenes: false
   resample_dataset_size: 500 # Number of unique scenes to sample from
   resample_interval: 2_000_000
-  sample_with_replacement: true
+  sample_with_replacement: false
   shuffle_dataset: true
   file_prefix: ""
 
@@ -102,18 +107,18 @@ train:
     num_parameters: 0 # Total trainable parameters, to be filled at runtime
 
   # # # Checkpointing # # #
-  checkpoint_interval: 250 # Save policy every k iterations
+  checkpoint_interval: 50 # Save policy every k iterations
   checkpoint_path: "./runs"
 
   # # # Rendering # # #
-  render: true # Determines whether to render the environment (note: will slow down training)
+  render: false # Determines whether to render the environment (note: will slow down training)
   render_3d: false # Render simulator state in 3d or 2d
-  render_interval: 200 # Render every k iterations
+  render_interval: 300 # Render every k iterations
   render_k_scenarios: 1 # Number of scenarios to render
   render_format: "mp4" # Options: gif, mp4
   render_fps: 20 # Frames per second
   zoom_radius: 100
-  plot_waypoints: true
+  plot_guidance_pos_xy: true
 
 vec:
   backend: "native" # Only native is currently supported
diff --git a/baselines/ppo/config/ppo_population.yaml b/baselines/ppo/config/ppo_population.yaml
index 4ff05427c..5d02aa13c 100644
--- a/baselines/ppo/config/ppo_population.yaml
+++ b/baselines/ppo/config/ppo_population.yaml
@@ -17,7 +17,7 @@ environment: # Overrides default environment configs (see pygpudrive/env/config.
   norm_obs: true
   remove_non_vehicles: false # If false, all agents are included (vehicles, pedestrians, cyclists)
   lidar_obs: false # NOTE: Setting this to true currently turns of the other observation types
-  reward_type: "reward_conditioned" # Options: "weighted_combination", "reward_conditioned", "follow_waypoints"
+  reward_type: "reward_conditioned" # Options: "weighted_combination", "reward_conditioned", "guided_autonomy"
   collision_weight: -0.75
   off_road_weight: -0.75
   goal_achieved_weight: 1.0
@@ -110,7 +110,7 @@ train:
   render_format: "mp4" # Options: gif, mp4
   render_fps: 20 # Frames per second
   zoom_radius: 100
-  plot_waypoints: true
+  plot_guidance_pos_xy: true
 
 vec:
   backend: "native" # Only native is currently supported
diff --git a/baselines/ppo/ppo_waypoint.py b/baselines/ppo/ppo_guided_autonomy.py
similarity index 94%
rename from baselines/ppo/ppo_waypoint.py
rename to baselines/ppo/ppo_guided_autonomy.py
index 621055635..8f6c2d8c9 100644
--- a/baselines/ppo/ppo_waypoint.py
+++ b/baselines/ppo/ppo_guided_autonomy.py
@@ -106,7 +106,7 @@ def init_wandb(args, name, id=None, resume=True):
 def run(
     config_path: Annotated[
         str, typer.Argument(help="The path to the default configuration file")
-    ] = "baselines/ppo/config/ppo_waypoint.yaml",
+    ] = "baselines/ppo/config/ppo_guided_autonomy.yaml",
     *,
     # fmt: off
     # Environment options
@@ -115,10 +115,10 @@ def run(
     k_unique_scenes: Annotated[Optional[int], typer.Option(help="The number of unique scenes to sample")] = None,
     collision_weight: Annotated[Optional[float], typer.Option(help="The weight for collision penalty")] = None,
     off_road_weight: Annotated[Optional[float], typer.Option(help="The weight for off-road penalty")] = None,
-    goal_achieved_weight: Annotated[Optional[float], typer.Option(help="The weight for goal-achieved reward")] = None,
-    waypoint_distance_scale: Annotated[Optional[float], typer.Option(help="Scale for realism rewards")] = None,
-    speed_distance_scale: Annotated[Optional[float], typer.Option(help="Scale for realism rewards")] = None,
-    jerk_smoothness_scale: Annotated[Optional[float], typer.Option(help="Scale for realism rewards")] = None,
+    guidance_pos_xy_weight: Annotated[Optional[float], typer.Option(help="Scale for realism rewards")] = None,
+    guidance_speed_weight: Annotated[Optional[float], typer.Option(help="Scale for realism rewards")] = None,
+    guidance_heading_weight: Annotated[Optional[float], typer.Option(help="Scale for realism rewards")] = None,
+    smoothness_weight: Annotated[Optional[float], typer.Option(help="Scale for realism rewards")] = None,
     dist_to_goal_threshold: Annotated[Optional[float], typer.Option(help="The distance threshold for goal-achieved")] = None,
     randomize_rewards: Annotated[Optional[int], typer.Option(help="If reward_type == reward_conditioned, choose the condition_mode; 0 or 1")] = 0,
     sampling_seed: Annotated[Optional[int], typer.Option(help="The seed for sampling scenes")] = None,
@@ -130,7 +130,7 @@ def run(
     vbd_trajectory_weight: Annotated[Optional[float], typer.Option(help="Weight for VBD trajectory deviation penalty")] = 0.1,
     vbd_in_obs: Annotated[Optional[bool], typer.Option(help="Include VBD predictions in the observation")] = False,
     init_steps: Annotated[Optional[int], typer.Option(help="Environment warmup steps")] = 0,
-    
+
     # Train options
     seed: Annotated[Optional[int], typer.Option(help="The seed for training")] = None,
     learning_rate: Annotated[Optional[float], typer.Option(help="The learning rate for training")] = None,
@@ -174,10 +174,10 @@ def run(
         "k_unique_scenes": k_unique_scenes,
         "collision_weight": collision_weight,
         "off_road_weight": off_road_weight,
-        "goal_achieved_weight": goal_achieved_weight,
-        "waypoint_distance_scale": waypoint_distance_scale,
-        "jerk_smoothness_scale": jerk_smoothness_scale,
-        "speed_distance_scale": speed_distance_scale,
+        "guidance_pos_xy_weight": guidance_pos_xy_weight,
+        "smoothness_weight": smoothness_weight,
+        "guidance_speed_weight": guidance_speed_weight,
+        "guidance_heading_weight": guidance_heading_weight,
         "dist_to_goal_threshold": dist_to_goal_threshold,
         "sampling_seed": sampling_seed,
         "obs_radius": obs_radius,
diff --git a/checkpoints/model_guidance_log_replay__S_1__04_26_09_02_20_677_000833.pt b/checkpoints/model_guidance_log_replay__S_1__04_26_09_02_20_677_000833.pt
new file mode 100644
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diff --git a/checkpoints/model_guidance_log_replay__S_3__04_27_13_13_33_780_013762.pt b/checkpoints/model_guidance_log_replay__S_3__04_27_13_13_33_780_013762.pt
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diff --git a/checkpoints/model_waypoint_rs__S_1__04_23_19_37_26_618_003500.pt b/checkpoints/model_waypoint_rs__S_1__04_23_19_37_26_618_003500.pt
deleted file mode 100644
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diff --git a/examples/eval/README.md b/examples/eval/README.md
index 78c321e8e..407f53789 100644
--- a/examples/eval/README.md
+++ b/examples/eval/README.md
@@ -2,9 +2,13 @@
 
 
 ## Requirements
-Prerequisite
+Prerequisite to run the eval
+```
+pip install --no-deps waymo-open-dataset-tf-2-12-0==1.6.6
+```
+
+Requirement to process the data
 ```
-pip install --no-deps waymo-open-dataset-tf-2-12-0==1.6.4
 pip install --no-deps git+https://github.com/waymo-research/waymax.git@main#egg=waymo-waymax
 ```
 
@@ -24,5 +28,5 @@ python examples/eval/extract_dataset.py --data_dir data/raw --save_dir data/proc
 ## Evaluation
 Run eval with
 ```
-python wosac_eval.py
+python run_wosac_eval.py
 ```
diff --git a/examples/eval/run_wosac_eval.py b/examples/eval/run_wosac_eval.py
index c08fa9e36..a939cc8aa 100644
--- a/examples/eval/run_wosac_eval.py
+++ b/examples/eval/run_wosac_eval.py
@@ -3,20 +3,28 @@
 import os
 import sys
 import mediapy
+import logging
+import numpy as np
+from time import perf_counter
 from tqdm import tqdm
+from pathlib import Path
 
 from gpudrive.env.config import EnvConfig
 from gpudrive.env.env_torch import GPUDriveTorchEnv
 from gpudrive.env.dataset import SceneDataLoader
 from gpudrive.datatypes.observation import GlobalEgoState
 from gpudrive.utils.checkpoint import load_agent
+from gpudrive.visualize.utils import img_from_fig
 
 # WOSAC
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 from waymo_open_dataset.protos import sim_agents_submission_pb2
-# from eval.wosac_eval import WOSACMetrics
 from eval.wosac_eval_origin import WOSACMetrics
 
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("WOSAC evaluation")
+
 
 def get_state(env):
     """Obtain raw agent states."""
@@ -38,18 +46,34 @@ def get_state(env):
 
 
 def rollout(
-    env,
-    sim_agent,
-    init_steps,
-    num_envs,
-    max_agents,
-    device,
+    env: GPUDriveTorchEnv,
+    sim_agent: torch.nn.Module,
+    init_steps: int,
+    num_envs: int,
+    max_agents: int,
+    device: str,
+    render_simulator_states: bool = False,
+    render_agent_pov: bool = False,
+    render_every_n_steps: int = 5,
+    save_videos: bool = True,
+    video_dir: str = "videos",
+    video_format: str = "gif",
 ):
     """Rollout agent in the environment and return the scenario rollouts."""
+    # Storage
+    env_ids = list(range(num_envs))
+    simulator_state_frames = {env_id: [] for env_id in range(num_envs)}
+    agent_observation_frames = {env_id: [] for env_id in range(num_envs)}
+
+    start_env_rollout = perf_counter()
+
+    control_mask = env.cont_agent_mask
 
-    next_obs = env.reset()
+    next_obs = env.reset(control_mask)
 
-    scenario_ids = list(env.get_scenario_ids().values())
+    # Get scenario ids
+    scenario_ids_dict = env.get_scenario_ids()
+    scenario_ids = list(scenario_ids_dict.values())
 
     pos_x_list = []
     pos_y_list = []
@@ -57,14 +81,12 @@ def rollout(
     heading_list = []
     done_list = [env.get_dones()]
 
-    control_mask = env.cont_agent_mask
-
     pos_x, pos_y, pos_z, heading, _ = get_state(env)
 
     for time_step in range(env.episode_len - init_steps):
 
         # Predict actions
-        action, _, _, _ = sim_agent(next_obs[control_mask])
+        action, _, _, _ = sim_agent(next_obs)
 
         action_template = torch.zeros(
             (num_envs, max_agents), dtype=torch.int64, device=device
@@ -74,7 +96,30 @@ def rollout(
         # Step
         env.step_dynamics(action_template)
 
-        next_obs = env.get_obs()
+        # Render
+        if render_simulator_states and time_step % render_every_n_steps == 0:
+            sim_states = env.vis.plot_simulator_state(
+                env_indices=env_ids,
+                zoom_radius=100,
+                time_steps=[time_step] * len(env_ids),
+                plot_guidance_pos_xy=True,
+            )
+            for idx in range(num_envs):
+                simulator_state_frames[idx].append(
+                    img_from_fig(sim_states[idx])
+                )
+
+        if render_agent_pov and time_step % render_every_n_steps == 0:
+            agent_obs = env.vis.plot_agent_observation(
+                env_idx=0,
+                agent_idx=0,
+                figsize=(10, 10),
+                trajectory=env.reference_path[0, :, :].to("cpu"),
+            )
+            agent_observation_frames[idx].append(img_from_fig(agent_obs))
+
+        # Get next observation
+        next_obs = env.get_obs(control_mask)
         done = env.get_dones()
 
         pos_x, pos_y, pos_z, heading, id = get_state(env)
@@ -86,6 +131,29 @@ def rollout(
         done_list.append(done)
     _ = done_list.pop()
 
+    if save_videos:
+        for idx in range(num_envs):
+            scenario_id = scenario_ids_dict[idx]
+            if (
+                render_simulator_states
+                and len(simulator_state_frames[idx]) > 0
+            ):
+                mediapy.write_video(
+                    f"{video_dir}/sim_state_env_{idx}_{scenario_id}.{video_format}",
+                    np.array(simulator_state_frames[idx]),
+                    fps=8,
+                    codec=video_format,
+                )
+
+        if render_agent_pov and len(agent_observation_frames[0]) > 0:
+            scenario_id = scenario_ids_dict[0]
+            mediapy.write_video(
+                f"{video_dir}/agent_0_{scenario_id}.{video_format}",
+                np.array(agent_observation_frames[0]),
+                fps=8,
+                codec=video_format,
+            )
+
     # Generate Scenario
     pos_x_stack = torch.stack(pos_x_list, dim=-1).cpu().numpy()
     pos_y_stack = torch.stack(pos_y_list, dim=-1).cpu().numpy()
@@ -95,6 +163,12 @@ def rollout(
     id = id.cpu().numpy()
     control_mask = control_mask.cpu().numpy()
 
+    logging.info(
+        f"Policy rollout took: {perf_counter() - start_env_rollout:.2f} s ({len(env.data_batch)} scenarios)."
+    )
+
+    start_ground_truth_ext = perf_counter()
+
     scenario_rollouts = []
     scenario_rollout_masks = []
     for i, scenario_id in enumerate(scenario_ids):
@@ -129,6 +203,10 @@ def rollout(
             )
         )
 
+    logging.info(
+        f"Ground truth extraction took: {perf_counter() - start_ground_truth_ext:.2f} s ({len(env.data_batch)} scenarios)."
+    )
+
     return scenario_ids, scenario_rollouts, scenario_rollout_masks
 
 
@@ -136,15 +214,16 @@ def rollout(
 
     # Settings
     MAX_AGENTS = 64
-    NUM_ENVS = 1
-    DEVICE = "cpu"
-    NUM_BATCHES = 1
-    NUM_ROLLOUTS = 10
+    NUM_ENVS = 3
+    DEVICE = "cuda"  # where to run the env rollouts
+    NUM_ROLLOUTS_PER_BATCH = 1
+    NUM_DATA_BATCHES = 2
     INIT_STEPS = 10
     DATASET_SIZE = 100
+    RENDER = True
 
-    DATA_JSON = "data/processed/wosac/validation_json_1"
-    DATA_TFRECORD = "data/processed/wosac/validation_tfrecord_1"
+    DATA_JSON = "data/processed/wosac/validation_json_3"
+    DATA_TFRECORD = "data/processed/wosac/validation_tfrecord_3"
 
     # Create data loader
     val_loader = SceneDataLoader(
@@ -152,58 +231,52 @@ def rollout(
         batch_size=NUM_ENVS,
         dataset_size=DATASET_SIZE,
         sample_with_replacement=True,
+        shuffle=True,
         file_prefix="",
     )
 
     # Load agent
     agent = load_agent(
-        path_to_cpt="checkpoints/model_waypoint_rs__S_1__04_23_19_37_26_618_003500.pt",
+        # path_to_cpt="checkpoints/model_guidance_log_replay__S_1__04_26_09_02_20_677_000833.pt",
+        path_to_cpt="checkpoints/model_guidance_log_replay__S_3__04_27_13_13_33_780_013762.pt",
+    ).to(DEVICE)
+
+    # Override default environment settings to match those the agent was trained with
+    default_config = EnvConfig()
+    config_dict = {
+        field.name: getattr(agent.config, field.name)
+        for field in dataclasses.fields(EnvConfig)
+        if hasattr(agent.config, field.name)
+        and getattr(agent.config, field.name)
+        != getattr(default_config, field.name)
+    }
+
+    # Add fixed overrides specific to WOSAC evaluation
+    fixed_overrides = {
+        "init_steps": INIT_STEPS,
+    }
+
+    logging.info(
+        f"initializing env with init_mode = {config_dict['init_mode']}"
     )
 
-    # Obtain config directly from the agent checkpoint
-    config = agent.config
-
-    # Configs
     env_config = dataclasses.replace(
-        EnvConfig(),
-        ego_state=config.ego_state,
-        road_map_obs=config.road_map_obs,
-        partner_obs=config.partner_obs,
-        reward_type=config.reward_type,
-        norm_obs=config.norm_obs,
-        dynamics_model=config.dynamics_model,
-        collision_behavior=config.collision_behavior,
-        polyline_reduction_threshold=config.polyline_reduction_threshold,
-        obs_radius=config.obs_radius,
-        steer_actions=torch.round(
-            torch.linspace(
-                -torch.pi / 3, torch.pi / 3, config.action_space_steer_disc
-            ),
-            decimals=3,
-        ),
-        accel_actions=torch.round(
-            torch.linspace(-4.0, 4.0, config.action_space_accel_disc),
-            decimals=3,
-        ),
-        remove_non_vehicles=config.remove_non_vehicles,
-        init_mode="womd_tracks_to_predict",
-        init_steps=INIT_STEPS,
-        goal_behavior="stop",
-        add_reference_path=config.add_reference_path,
-        add_reference_speed=config.add_reference_speed,
+        default_config, **config_dict, **fixed_overrides
     )
 
+    # Make environment
     env = GPUDriveTorchEnv(
         config=env_config,
         data_loader=val_loader,
         max_cont_agents=MAX_AGENTS,
         device=DEVICE,
     )
+
     wosac_metrics = WOSACMetrics()
 
-    for _ in tqdm(range(NUM_BATCHES)):
-        for _ in range(NUM_ROLLOUTS):
-            # try:
+    for _ in tqdm(range(NUM_DATA_BATCHES)):
+        for _ in range(NUM_ROLLOUTS_PER_BATCH):
+
             scenario_ids, scenario_rollouts, scenario_rollout_masks = rollout(
                 env=env,
                 sim_agent=agent,
@@ -211,11 +284,10 @@ def rollout(
                 num_envs=NUM_ENVS,
                 max_agents=MAX_AGENTS,
                 device=DEVICE,
+                render_simulator_states=RENDER,
+                render_agent_pov=RENDER,
+                save_videos=RENDER,
             )
-            # except Exception as e:
-            #     print(f"Error during rollout: {e}")
-            #     continue
-
             tf_record_paths = [
                 os.path.join(DATA_TFRECORD, f"{scenario_id}.tfrecords")
                 for scenario_id in scenario_ids
@@ -225,11 +297,11 @@ def rollout(
                 scenario_rollouts,
                 # scenario_rollout_masks=scenario_rollout_masks
             )
-        try:
-            env.swap_data_batch()
-        except Exception as e:
-            break
 
+        # Swap batch of scenarios
+        env.swap_data_batch()
+
+    # Aggregate results
     results = wosac_metrics.compute()
 
     for key, value in results.items():
diff --git a/examples/eval/wosac_eval_origin.py b/examples/eval/wosac_eval_origin.py
index 943b4e8da..9bc0f57f8 100644
--- a/examples/eval/wosac_eval_origin.py
+++ b/examples/eval/wosac_eval_origin.py
@@ -1,15 +1,16 @@
 import itertools
 import multiprocessing as mp
 import os
+import datetime
+import pandas as pd
+import numpy as np
 from pathlib import Path
-from typing import Dict, List
+from typing import Dict, List, Optional
 
 import tensorflow as tf
 import waymo_open_dataset.wdl_limited.sim_agents_metrics.metrics as wosac_metrics
 from waymo_open_dataset.utils.sim_agents import submission_specs
 
-# import eval.wosac_metrics.metrics as wosac_metrics
-
 from google.protobuf import text_format
 from torch import Tensor, tensor
 from torchmetrics import Metric
@@ -25,11 +26,20 @@ class WOSACMetrics(Metric):
     validation metrics based on ground truth trajectory, using waymo_open_dataset api
     """
 
-    def __init__(self, challenge_type = None, prefix: str = "", ego_only: bool = False) -> None:
+    def __init__(
+        self,
+        challenge_type=None,
+        prefix: str = "",
+        ego_only: bool = False,
+        baselines_df: Optional[pd.DataFrame] = None,
+        save_table_with_baselines: bool = True,
+    ) -> None:
         super().__init__()
         self.is_mp_init = False
         self.prefix = prefix
         self.ego_only = ego_only
+        self.save_table_with_baselines = save_table_with_baselines
+
         if challenge_type is None:
             self.challenge_type = submission_specs.ChallengeType.SIM_AGENTS
         else:
@@ -37,6 +47,14 @@ def __init__(self, challenge_type = None, prefix: str = "", ego_only: bool = Fal
 
         self.wosac_config = load_metrics_config(self.challenge_type)
 
+        # Initialize baseline df if not provided
+        if baselines_df is None:
+            self.baselines_df = self._create_baselines_df()
+        else:
+            self.baselines_df = baselines_df
+
+        self.timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H")
+
         self.field_names = [
             "metametric",
             "average_displacement_error",
@@ -56,15 +74,111 @@ def __init__(self, challenge_type = None, prefix: str = "", ego_only: bool = Fal
         ]
         for k in self.field_names:
             self.add_state(k, default=tensor(0.0), dist_reduce_fx="sum")
-        self.add_state("scenario_counter", default=tensor(0.0), dist_reduce_fx="sum")
+        self.add_state(
+            "scenario_counter", default=tensor(0.0), dist_reduce_fx="sum"
+        )
         tf.config.set_visible_devices([], "GPU")
 
+    def _create_baselines_df(self) -> pd.DataFrame:
+        """Create a DataFrame with baseline metrics based on the provided table."""
+        columns = [
+            "AGENT POLICY",
+            "REPLAN RATE (Hz)",
+            "LINEAR SPEED (↑)",
+            "LINEAR ACCEL. (↑)",
+            "ANG. SPEED (↑)",
+            "ANG. ACCEL. (↑)",
+            "DIST. TO OBJ. (↑)",
+            "COLLISION (↑)",
+            "TTC (↑)",
+            "DIST. TO ROAD EDGE (↑)",
+            "OFFROAD (↑)",
+            "COMPOSITE METRIC (↑)",
+            "ADE (↓)",
+            "MINADE (↓)",
+            "COLLISION RATE (↓)",
+            "OFFROAD RATE (↓)",
+        ]
+
+        data = [
+            # Logged Oracle values
+            [
+                "Logged oracle",
+                "-",
+                0.476,
+                0.478,
+                0.578,
+                0.694,
+                0.476,
+                1.000,
+                0.883,
+                0.715,
+                1.000,
+                0.819,
+                0.000,
+                0.000,
+                0.028,
+                0.111,
+            ],
+            # Versatile Behavior Diffusion
+            [
+                "VBD",
+                0.125,
+                0.359,
+                0.366,
+                0.420,
+                0.522,
+                0.368,
+                0.934,
+                0.815,
+                0.651,
+                0.879,
+                0.720,
+                2.257,
+                1.474,
+                0.036,
+                0.152,
+            ],
+        ]
+
+        # Other rows from the table can be added as needed
+        data.extend(
+            [
+                [
+                    "Random agent",
+                    10,
+                    0.002,
+                    0.116,
+                    0.014,
+                    0.034,
+                    0.000,
+                    0.000,
+                    0.735,
+                    0.148,
+                    0.191,
+                    0.144,
+                    50.739,
+                    50.706,
+                    1.000,
+                    0.613,
+                ],
+            ]
+        )
+
+        return pd.DataFrame(data, columns=columns)
+
     @staticmethod
     def _compute_scenario_metrics(
-        config, scenario_file, scenario_rollout, ego_only, scenario_rollouts_mask=None
+        config,
+        scenario_file,
+        scenario_rollout,
+        ego_only,
+        scenario_rollouts_mask=None,
     ) -> sim_agents_metrics_pb2.SimAgentMetrics:
         scenario = scenario_pb2.Scenario()
-        for data in tf.data.TFRecordDataset([scenario_file], compression_type=""):
+        for data in tf.data.TFRecordDataset(
+            [scenario_file], compression_type=""
+        ):
             scenario.ParseFromString(bytes(data.numpy()))
             break
         if ego_only:
@@ -74,32 +188,43 @@ def _compute_scenario_metrics(
                         scenario.tracks[i].states[t].valid = False
             while len(scenario.tracks_to_predict) > 1:
                 scenario.tracks_to_predict.pop()
-            scenario.tracks_to_predict[0].track_index = scenario.sdc_track_index
+            scenario.tracks_to_predict[
+                0
+            ].track_index = scenario.sdc_track_index
 
         return wosac_metrics.compute_scenario_metrics_for_bundle(
-            config, scenario, scenario_rollout,# scenario_rollouts_mask=scenario_rollouts_mask
+            config,
+            scenario,
+            scenario_rollout,  # scenario_rollouts_mask=scenario_rollouts_mask
         )
 
     def update(
         self,
         scenario_files: List[str],
         scenario_rollouts: List[sim_agents_submission_pb2.ScenarioRollouts],
-        scenario_rollout_masks = None,
+        scenario_rollout_masks=None,
     ) -> None:
 
         if scenario_rollout_masks is None:
             scenario_rollout_masks = [None] * len(scenario_rollouts)
         pool_scenario_metrics = []
-        for _scenario, _scenario_rollout, _scenario_mask in zip(scenario_files, scenario_rollouts, scenario_rollout_masks):
+        for _scenario, _scenario_rollout, _scenario_mask in zip(
+            scenario_files, scenario_rollouts, scenario_rollout_masks
+        ):
             try:
                 pool_scenario_metrics.append(
                     self._compute_scenario_metrics(
-                        self.wosac_config, _scenario, _scenario_rollout, self.ego_only,
+                        self.wosac_config,
+                        _scenario,
+                        _scenario_rollout,
+                        self.ego_only,
                         # scenario_rollouts_mask=_scenario_mask
                     )
                 )
             except Exception as e:
-                print(f"Error processing scenario {_scenario_rollout.scenario_id}")
+                print(
+                    f"Error processing scenario {_scenario_rollout.scenario_id}"
+                )
                 print(e)
 
         for scenario_metrics in pool_scenario_metrics:
@@ -108,11 +233,15 @@ def update(
             self.average_displacement_error += (
                 scenario_metrics.average_displacement_error
             )
-            self.linear_speed_likelihood += scenario_metrics.linear_speed_likelihood
+            self.linear_speed_likelihood += (
+                scenario_metrics.linear_speed_likelihood
+            )
             self.linear_acceleration_likelihood += (
                 scenario_metrics.linear_acceleration_likelihood
             )
-            self.angular_speed_likelihood += scenario_metrics.angular_speed_likelihood
+            self.angular_speed_likelihood += (
+                scenario_metrics.angular_speed_likelihood
+            )
             self.angular_acceleration_likelihood += (
                 scenario_metrics.angular_acceleration_likelihood
             )
@@ -137,8 +266,12 @@ def update(
             self.traffic_light_violation_likelihood += (
                 scenario_metrics.traffic_light_violation_likelihood
             )
-            self.simulated_collision_rate += scenario_metrics.simulated_collision_rate
-            self.simulated_offroad_rate += scenario_metrics.simulated_offroad_rate
+            self.simulated_collision_rate += (
+                scenario_metrics.simulated_collision_rate
+            )
+            self.simulated_offroad_rate += (
+                scenario_metrics.simulated_offroad_rate
+            )
 
     def compute(self) -> Dict[str, Tensor]:
         metrics_dict = {}
@@ -163,22 +296,76 @@ def compute(self) -> Dict[str, Tensor]:
         for k in self.field_names:
             out_dict[f"{self.prefix}/wosac_likelihood/{k}"] = metrics_dict[k]
 
+        if self.save_table_with_baselines:
+            self._add_current_method_to_baselines(metrics_dict, final_metrics)
+
+            table_name = f"wosac_table_{self.timestamp}.csv"
+            self.baselines_df.to_csv(table_name, index=False)
+            print(f"Saved df to {table_name}")
+
         return out_dict
 
+    def _add_current_method_to_baselines(self, metrics_dict, final_metrics):
+        """Add the current method's metrics to the baselines dataframe."""
+        new_row = {
+            "AGENT POLICY": "Guided self-play (ours)",
+            "REPLAN RATE (Hz)": 10,
+            "LINEAR SPEED (↑)": metrics_dict["linear_speed_likelihood"].item(),
+            "LINEAR ACCEL. (↑)": metrics_dict[
+                "linear_acceleration_likelihood"
+            ].item(),
+            "ANG. SPEED (↑)": metrics_dict["angular_speed_likelihood"].item(),
+            "ANG. ACCEL. (↑)": metrics_dict[
+                "angular_acceleration_likelihood"
+            ].item(),
+            "DIST. TO OBJ. (↑)": metrics_dict[
+                "distance_to_nearest_object_likelihood"
+            ].item(),
+            "COLLISION (↑)": metrics_dict[
+                "collision_indication_likelihood"
+            ].item(),
+            "TTC (↑)": metrics_dict["time_to_collision_likelihood"].item(),
+            "DIST. TO ROAD EDGE (↑)": metrics_dict[
+                "distance_to_road_edge_likelihood"
+            ].item(),
+            "OFFROAD (↑)": metrics_dict[
+                "offroad_indication_likelihood"
+            ].item(),
+            "COMPOSITE METRIC (↑)": final_metrics.realism_meta_metric,
+            "ADE (↓)": metrics_dict["average_displacement_error"].item(),
+            "MINADE (↓)": metrics_dict[
+                "min_average_displacement_error"
+            ].item(),
+            "COLLISION RATE (↓)": metrics_dict[
+                "simulated_collision_rate"
+            ].item(),
+            "OFFROAD RATE (↓)": metrics_dict["simulated_offroad_rate"].item(),
+        }
+
+        self.baselines_df = pd.concat(
+            [self.baselines_df, pd.DataFrame([new_row])], ignore_index=True
+        )
+
+        # Optional: Sort the dataframe by a performance metric, such as COMPOSITE METRIC
+        self.baselines_df = self.baselines_df.sort_values(
+            by="COMPOSITE METRIC (↑)", ascending=False
+        )
+
 
 def load_metrics_config(
     challenge_type: submission_specs.ChallengeType,
 ) -> sim_agents_metrics_pb2.SimAgentMetricsConfig:
     """Loads the `SimAgentMetricsConfig` used for the challenge."""
     import waymo_open_dataset
+
     pyglib_resource = waymo_open_dataset.__path__[0]
     if challenge_type == submission_specs.ChallengeType.SIM_AGENTS:
-        config_path = f'{pyglib_resource}/wdl_limited/sim_agents_metrics/challenge_2025_sim_agents_config.textproto' # pylint: disable=line-too-long
+        config_path = f"{pyglib_resource}/wdl_limited/sim_agents_metrics/challenge_2025_sim_agents_config.textproto"  # pylint: disable=line-too-long
     elif challenge_type == submission_specs.ChallengeType.SCENARIO_GEN:
-        config_path = f'{pyglib_resource}/wdl_limited/sim_agents_metrics/challenge_2025_scenario_gen_config.textproto'  # pylint: disable=line-too-long
+        config_path = f"{pyglib_resource}/wdl_limited/sim_agents_metrics/challenge_2025_scenario_gen_config.textproto"  # pylint: disable=line-too-long
     else:
-        raise ValueError(f'Unsupported {challenge_type=}')
-    with open(config_path, 'r') as f:
+        raise ValueError(f"Unsupported {challenge_type=}")
+    with open(config_path, "r") as f:
         config = sim_agents_metrics_pb2.SimAgentMetricsConfig()
         text_format.Parse(f.read(), config)
-    return config
\ No newline at end of file
+    return config
diff --git a/examples/experimental/notebooks/debug.ipynb b/examples/experimental/notebooks/debug.ipynb
index 2be729d0a..f4fd484c2 100644
--- a/examples/experimental/notebooks/debug.ipynb
+++ b/examples/experimental/notebooks/debug.ipynb
@@ -31,13 +31,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "env_config = EnvConfig(\n",
     "    dynamics_model=\"classic\",\n",
-    "    reward_type=\"follow_waypoints\",\n",
+    "    reward_type=\"guided_autonomy\",\n",
     "    add_reference_path=True,\n",
     ")\n",
     "render_config = RenderConfig()\n",
diff --git a/gpudrive/datatypes/trajectory.py b/gpudrive/datatypes/trajectory.py
index 92541571b..99fbf72b8 100644
--- a/gpudrive/datatypes/trajectory.py
+++ b/gpudrive/datatypes/trajectory.py
@@ -112,7 +112,12 @@ def comp_reference_speed(self):
         """Returns the average speed of the trajectory."""
         return torch.sqrt(
             self.vel_xy[:, :, :, 0] ** 2 + self.vel_xy[:, :, :, 1] ** 2
-        )
+        ).unsqueeze(-1)
+
+    @property
+    def length(self):
+        """Returns the length of the trajectory."""
+        return self.pos_xy.shape[2]
 
 
 @dataclass
@@ -128,12 +133,16 @@ class VBDTrajectory:
 
     def __init__(self, vbd_traj_tensor: torch.Tensor):
         """Initializes the VBD trajectory with a tensor."""
-        self.pos_x = vbd_traj_tensor[:, :, :, 0]
-        self.pos_y = vbd_traj_tensor[:, :, :, 1]
-        self.pos_xy = torch.stack([self.pos_x, self.pos_y], dim=3)
-        self.yaw = vbd_traj_tensor[:, :, :, 2]
-        self.vel_x = vbd_traj_tensor[:, :, :, 3]
-        self.vel_y = vbd_traj_tensor[:, :, :, 4]
+        self.pos_x = vbd_traj_tensor[:, :, :, 0].unsqueeze(-1)
+        self.pos_y = vbd_traj_tensor[:, :, :, 1].unsqueeze(-1)
+        self.pos_xy = vbd_traj_tensor[:, :, :, :2]
+        self.yaw = vbd_traj_tensor[:, :, :, 2].unsqueeze(-1)
+        self.vel_x = vbd_traj_tensor[:, :, :, 3].unsqueeze(-1)
+        self.vel_y = vbd_traj_tensor[:, :, :, 4].unsqueeze(-1)
+        self.vel_xy = vbd_traj_tensor[:, :, :, 3:5]
+        self.ref_speed = self.comp_reference_speed()
+        # Assumption: All timesteps are valid
+        self.valids = torch.ones_like(self.pos_x, dtype=torch.int32)
 
     @classmethod
     def from_tensor(
@@ -148,6 +157,15 @@ def from_tensor(
         elif backend == "jax":
             raise NotImplementedError("JAX backend not implemented yet.")
 
+    def comp_reference_speed(self):
+        """Returns the average speed of the trajectory."""
+        return torch.sqrt(self.vel_x**2 + self.vel_y**2)
+
+    @property
+    def length(self):
+        """Returns the length of the trajectory."""
+        return self.pos_xy.shape[2]
+
     # def restore_mean(self, mean_x, mean_y):
     #     """Reapplies the mean to revert back to the original coordinates."""
     #     # Reshape for broadcasting
diff --git a/gpudrive/env/base_env.py b/gpudrive/env/base_env.py
index 7c5f6e273..7c5afc16b 100755
--- a/gpudrive/env/base_env.py
+++ b/gpudrive/env/base_env.py
@@ -62,7 +62,7 @@ def _set_reward_params(self):
         if (
             self.config.reward_type == "sparse_on_goal_achieved"
             or self.config.reward_type == "weighted_combination"
-            or self.config.reward_type == "follow_waypoints"
+            or self.config.reward_type == "guided_autonomy"
             or self.config.reward_type == "reward_conditioned"
         ):
             reward_params.rewardType = (
diff --git a/gpudrive/env/config.py b/gpudrive/env/config.py
index 186aca52c..b1fb1ddf0 100755
--- a/gpudrive/env/config.py
+++ b/gpudrive/env/config.py
@@ -30,6 +30,16 @@ class EnvConfig:
     partner_obs: bool = True  # Include partner vehicle info in observations
     bev_obs: bool = False  # Include rasterized Bird's Eye View observations centered on ego vehicle
     norm_obs: bool = True  # Normalize observations
+    add_previous_action: bool = False  # Previous action time agent has taken
+
+    # Guidance settings; these are used to direct the agent's behavior and
+    # will be included in the observations if set to True
+    guidance: bool = True
+    guidance_mode: str = "log_replay"  # Options: "log_replay", "vbd_amortized", "vbd_online", "goals_only"
+    # Ways to guide the agent
+    add_reference_pos_xy: bool = True  # (x, y) position time series
+    add_reference_speed: bool = False  # speed time series
+    add_reference_heading: bool = False  # heading time series
 
     # Maximum number of controlled agents in the scene
     max_controlled_agents: int = madrona_gpudrive.kMaxAgentCount
@@ -51,14 +61,18 @@ class EnvConfig:
     dynamics_model: str = (
         "classic"  # Options: "classic", "bicycle", "delta_local", or "state"
     )
-
+    
     # Action space settings (if discretized)
     # Classic or Invertible Bicycle dynamics model
+    action_space_steer_disc: int = 13
+    action_space_accel_disc: int = 7
+    max_steer_angle: float = 1.57  # in radians: pi/2 = 1.57, pi/3 = 1.05
+    max_accel_value: float = 4.0
     steer_actions: torch.Tensor = torch.round(
-        torch.linspace(-torch.pi / 3, torch.pi / 3, 13), decimals=3
+        torch.linspace(-max_steer_angle, max_steer_angle, action_space_steer_disc), decimals=3
     )
     accel_actions: torch.Tensor = torch.round(
-        torch.linspace(-4.0, 4.0, 7), decimals=3
+        torch.linspace(-max_accel_value, max_accel_value, action_space_accel_disc), decimals=3
     )
     head_tilt_actions: torch.Tensor = torch.Tensor([0])
 
@@ -94,11 +108,6 @@ class EnvConfig:
     # Goal behavior settings
     goal_behavior: str = "ignore"  # Options: "stop", "ignore", "remove"
 
-    # Reference points settings
-    add_reference_speed: bool = (
-        False  # Include reference speed in observations
-    )
-    add_reference_path: bool = False
     prob_reference_dropout: float = (
         0.0  # Probability of dropping reference points
     )
@@ -106,15 +115,20 @@ class EnvConfig:
 
     # Reward settings
     reward_type: str = "sparse_on_goal_achieved"
-    # Alternatively, "weighted_combination", "follow_waypoints", "distance_to_vdb_trajs", "reward_conditioned"
+    # Alternatively, "weighted_combination", "guided_autonomy", "reward_conditioned"
 
-    # If reward_type is "follow_waypoints", the following parameters are used
-    waypoint_sample_interval: int = 1  # Interval for sampling waypoints
-    waypoint_distance_scale: float = (
-        0.01  # Importance of distance to waypoints
+    # If reward_type is "guided_autonomy", the following parameters are used
+    guidance_sample_interval: int = 1
+    guidance_pos_xy_weight: float = (
+        0.01  # Importance of matching suggested positions
+    )
+    guidance_speed_weight: float = (
+        0.01  # Importance of matching suggested speeds
+    )
+    guidance_heading_weight: float = (
+        0.0  # Importance of matching suggested headings
     )
-    speed_distance_scale: float = 0.0
-    jerk_smoothness_scale: float = 0.0
+    smoothness_weight: float = 0.0
 
     # If reward_type is "reward_conditioned", the following parameters are used
     # Weights for the reward components
diff --git a/gpudrive/env/constants.py b/gpudrive/env/constants.py
index f457cf223..3b71fd766 100644
--- a/gpudrive/env/constants.py
+++ b/gpudrive/env/constants.py
@@ -1,4 +1,5 @@
 import numpy as np
+import madrona_gpudrive
 
 """Predefined constants for the environment."""
 
@@ -22,12 +23,14 @@
 MAX_ROAD_SCALE = 100
 
 # Feature shape constants
-EGO_FEAT_DIM = 6  # Ego state base fields
+EGO_FEAT_DIM = 4  # Ego state base fields
 PARTNER_FEAT_DIM = 6
 ROAD_GRAPH_FEAT_DIM = 13
 
+ROAD_GRAPH_TOP_K = madrona_gpudrive.kMaxAgentMapObservationsCount
+
 # Dataset constants
-LOG_TRAJECTORY_LEN = 91
+LOG_TRAJECTORY_LENGTH = 91
 
 # BEV observation constants
 BEV_RASTERIZATION_RESOLUTION = 200
diff --git a/gpudrive/env/env_puffer.py b/gpudrive/env/env_puffer.py
index 630b26b19..8bd9ec3d3 100644
--- a/gpudrive/env/env_puffer.py
+++ b/gpudrive/env/env_puffer.py
@@ -168,19 +168,25 @@ def __init__(
         dynamics_model="classic",
         action_space_steer_disc=13,
         action_space_accel_disc=7,
+        max_steer_angle=1.57,
+        max_accel_value=4.0,
         ego_state=True,
         road_map_obs=True,
         partner_obs=True,
         norm_obs=True,
         lidar_obs=False,
         bev_obs=False,
-        add_reference_path=False,
-        add_reference_speed=False,
+        add_previous_action=False,
+        guidance=True,
+        add_reference_pos_xy=True,
+        add_reference_speed=True,
+        add_reference_heading=False,
         prob_reference_dropout=0.0,
         reward_type="weighted_combination",
-        waypoint_distance_scale=0.05,
-        speed_distance_scale=0.0,
-        jerk_smoothness_scale=0.0,
+        guidance_pos_xy_weight=0.01,
+        guidance_speed_weight=0.0,
+        guidance_heading_weight=0.0,
+        smoothness_weight=0.0,
         condition_mode="random",
         collision_behavior="ignore",
         goal_behavior="remove",
@@ -204,7 +210,7 @@ def __init__(
         render_format="mp4",
         render_fps=15,
         zoom_radius=50,
-        plot_waypoints=False,
+        plot_guidance_pos_xy=False,
         buf=None,
         **kwargs,
     ):
@@ -231,6 +237,12 @@ def __init__(
         self.init_mode = init_mode
         self.reward_type = reward_type
 
+        # Expert guidance
+        self.guidance = guidance
+        self.add_reference_pos_xy = add_reference_pos_xy
+        self.add_reference_speed = add_reference_speed
+        self.add_reference_heading = add_reference_heading
+
         self.render = render
         self.render_interval = render_interval
         self.render_k_scenarios = render_k_scenarios
@@ -238,7 +250,7 @@ def __init__(
         self.render_format = render_format
         self.render_fps = render_fps
         self.zoom_radius = zoom_radius
-        self.plot_waypoints = plot_waypoints
+        self.plot_guidance_pos_xy = plot_guidance_pos_xy
         self.track_realism_metrics = track_realism_metrics
         self.track_n_worlds = track_n_worlds
 
@@ -259,14 +271,18 @@ def __init__(
             road_map_obs=road_map_obs,
             partner_obs=partner_obs,
             reward_type=reward_type,
-            waypoint_distance_scale=waypoint_distance_scale,
-            speed_distance_scale=speed_distance_scale,
-            jerk_smoothness_scale=jerk_smoothness_scale,
+            guidance_pos_xy_weight=guidance_pos_xy_weight,
+            guidance_speed_weight=guidance_speed_weight,
+            guidance_heading_weight=guidance_heading_weight,
+            smoothness_weight=smoothness_weight,
             condition_mode=condition_mode,
             norm_obs=norm_obs,
             bev_obs=bev_obs,
-            add_reference_path=add_reference_path,
+            add_previous_action=add_previous_action,
+            guidance=guidance,
+            add_reference_pos_xy=add_reference_pos_xy,
             add_reference_speed=add_reference_speed,
+            add_reference_heading=add_reference_heading,
             prob_reference_dropout=prob_reference_dropout,
             dynamics_model=dynamics_model,
             collision_behavior=collision_behavior,
@@ -278,15 +294,10 @@ def __init__(
             lidar_obs=lidar_obs,
             disable_classic_obs=True if lidar_obs else False,
             obs_radius=obs_radius,
-            steer_actions=torch.round(
-                torch.linspace(
-                    -torch.pi / 3, torch.pi / 3, action_space_steer_disc
-                ),
-                decimals=3,
-            ),
-            accel_actions=torch.round(
-                torch.linspace(-4.0, 4.0, action_space_accel_disc), decimals=3
-            ),
+            max_steer_angle=max_steer_angle,
+            max_accel_value=max_accel_value,
+            action_space_steer_disc=action_space_steer_disc,
+            action_space_accel_disc=action_space_accel_disc,
             use_vbd=use_vbd,
             vbd_trajectory_weight=vbd_trajectory_weight,
         )
@@ -433,10 +444,10 @@ def step(self, action):
         reward_controlled = reward[self.controlled_agent_mask]
 
         # Store human-like and internal rewards separately
-        if self.reward_type == "follow_waypoints":
+        if self.reward_type == "guided_autonomy":
             self.human_like_rewards[
                 self.live_agent_mask
-            ] += self.env.distance_penalty[self.live_agent_mask]
+            ] += self.env.guidance_error[self.live_agent_mask]
             self.internal_rewards[
                 self.live_agent_mask
             ] += self.env.base_rewards[self.live_agent_mask]
@@ -648,7 +659,7 @@ def render_env(self):
                 env_indices=envs_to_render,
                 time_steps=time_steps,
                 zoom_radius=self.zoom_radius,
-                plot_waypoints=self.plot_waypoints,
+                plot_guidance_pos_xy=self.plot_guidance_pos_xy,
             )
 
             agent_obs = self.env.vis.plot_agent_observation(
@@ -775,7 +786,7 @@ def compute_realism_metrics(self, done_worlds):
         )
         # [batch, time, 1]
         valid_mask = (
-            self.env.log_trajectory.valids[done_worlds]
+            self.env.reference_trajectory.valids[done_worlds]
             .detach()
             .cpu()
             .numpy()[control_mask]
@@ -785,14 +796,14 @@ def compute_realism_metrics(self, done_worlds):
         # Take human logs (ground-truth)
         # Shape: [worlds, max_cont_agents, time, 2] -> [batch, time, 2]
         ref_pos_xy_np = (
-            self.env.log_trajectory.pos_xy[done_worlds]
+            self.env.reference_trajectory.pos_xy[done_worlds]
             .detach()
             .cpu()
             .numpy()[control_mask]
         )
         # Shape: [worlds, max_cont_agents, time, 1] -> [batch, time, 1]
         ref_headings_np = (
-            self.env.log_trajectory.yaw[done_worlds]
+            self.env.reference_trajectory.yaw[done_worlds]
             .detach()
             .cpu()
             .numpy()[control_mask]
diff --git a/gpudrive/env/env_torch.py b/gpudrive/env/env_torch.py
index 5db6d0d68..60cec0fef 100755
--- a/gpudrive/env/env_torch.py
+++ b/gpudrive/env/env_torch.py
@@ -12,8 +12,8 @@
 from gpudrive.env.base_env import GPUDriveGymEnv
 from gpudrive.datatypes.trajectory import (
     LogTrajectory,
-    to_local_frame,
     VBDTrajectory,
+    to_local_frame,
 )
 from gpudrive.datatypes.roadgraph import (
     LocalRoadGraphPoints,
@@ -79,6 +79,8 @@ def __init__(
         # Initialize simulator
         self.sim = self._initialize_simulator(params, self.data_batch)
 
+        self.init_steps = self.config.init_steps
+
         # Controlled agents setup
         self.cont_agent_mask = self.get_controlled_agents_mask()
         self.max_agent_count = self.cont_agent_mask.shape[1]
@@ -86,24 +88,14 @@ def __init__(
             self.cont_agent_mask.sum().item()
         )
 
-        self.log_trajectory = LogTrajectory.from_tensor(
-            self.sim.expert_trajectory_tensor(),
-            self.num_worlds,
-            self.max_agent_count,
-            backend=self.backend,
-            device=self.device,
-        )
+        self.setup_guidance()
+
         self.episode_len = self.config.episode_len
-        self.reference_path_length = self.log_trajectory.pos_xy.shape[2] 
         self.step_in_world = (
             self.episode_len - self.sim.steps_remaining_tensor().to_torch()
         )
 
-        # Now initialize reward weights tensor if using reward_conditioned reward type
-        if (
-            hasattr(self.config, "reward_type")
-            and self.config.reward_type == "reward_conditioned"
-        ):
+        if self.config.reward_type == "reward_conditioned":
             # Use default condition_mode from config or fall back to "random"
             condition_mode = getattr(self.config, "condition_mode", "random")
             self.agent_type = getattr(self.config, "agent_type", None)
@@ -115,9 +107,6 @@ def __init__(
             (self.num_worlds, self.max_cont_agents, 3), device=self.device
         )
 
-        # Initialize VBD model if used
-        self._initialize_vbd()
-
         # Setup action and observation spaces
         self.observation_space = Box(
             low=-1.0,
@@ -140,13 +129,40 @@ def __init__(
         self.vis = MatplotlibVisualizer(
             sim_object=self.sim,
             controlled_agent_mask=self.cont_agent_mask,
+            reference_trajectory=self.reference_trajectory,
             goal_radius=self.config.dist_to_goal_threshold,
-            backend=self.backend,
             num_worlds=self.num_worlds,
             render_config=self.render_config,
             env_config=self.config,
         )
 
+    def setup_guidance(self):
+        """Configure the reference trajectory based on the guidance mode."""
+        self.guidance_mode = self.config.guidance_mode
+
+        if self.guidance_mode == "vbd_amortized":
+            trajectory_tensor = self.sim.vbd_trajectory_tensor()
+            self.reference_trajectory = VBDTrajectory.from_tensor(
+                trajectory_tensor, self.backend, self.device
+            )
+        elif self.guidance_mode == "vbd_online":
+            # TODO: Add support for 'vbd_online' mode
+            raise ValueError(
+                f"Unsupported guidance mode: {self.guidance_mode}."
+            )
+        else:  # Default option is "log_replay"
+            trajectory_tensor = self.sim.expert_trajectory_tensor()
+            self.reference_trajectory = LogTrajectory.from_tensor(
+                trajectory_tensor,
+                self.num_worlds,
+                self.max_agent_count,
+                self.backend,
+                self.device,
+            )
+
+        # Length of the guidance trajectory
+        self.reference_traj_len = self.reference_trajectory.length
+
     def _initialize_vbd(self):
         """
         Initialize the Versatile Behavior Diffusion (VBD) model and related
@@ -161,6 +177,8 @@ def _initialize_vbd(self):
         if self.use_vbd:
             self._load_vbd_trajectories()
         else:
+            self.init_steps = self.config.init_steps
+
             self.vbd_trajectories = None
 
     def _generate_sample_batch(self, init_steps=10):
@@ -425,15 +443,9 @@ def reset(
 
         return self.get_obs(mask)
 
-    def get_dones(self, world_time_steps=None):
+    def get_dones(self):
         """
         Returns tensor indicating which agents have terminated.
-
-        Args:
-            world_time_steps: Optional tensor [num_worlds] with current timestep per world.
-
-        Returns:
-            torch.Tensor: Boolean tensor [num_worlds, num_agents] where True indicates done.
         """
         terminal = (
             self.sim.done_tensor()
@@ -442,28 +454,12 @@ def get_dones(self, world_time_steps=None):
             .squeeze(dim=2)
             .to(torch.float)
         )
-
-        if (
-            world_time_steps is not None
-            and self.config.reward_type == "follow_waypoints"
-            and self.config.waypoint_distance_scale > 0.0
-        ):
-            # Find last valid timestep for each agent, this is the ground-truth episode length
-            agent_episode_length = 90 - torch.argmax(
-                self.log_trajectory.valids.squeeze(-1).flip(2), dim=2
-            )
-
-            expanded_time_steps = world_time_steps.unsqueeze(1).expand_as(
-                agent_episode_length
-            )
-            return terminal.bool() & (
-                expanded_time_steps >= agent_episode_length
-            )
-
-        else:
-            return terminal.bool()
+        return terminal.bool()
 
     def get_infos(self):
+        """
+        Returns the info tensor for the current step.
+        """
         return Info.from_tensor(
             self.sim.info_tensor(),
             backend=self.backend,
@@ -473,7 +469,7 @@ def get_infos(self):
     def get_rewards(
         self,
         collision_weight=-0.5,
-        goal_achieved_weight=0.0,
+        goal_achieved_weight=1.0,
         off_road_weight=-0.5,
     ):
         """Obtain the rewards for the current step."""
@@ -527,95 +523,97 @@ def get_rewards(
 
             return weighted_rewards
 
-        elif self.config.reward_type == "distance_to_vdb_trajs":
-            # Reward based on distance to VBD predicted trajectories
-            # (i.e. the deviation from the predicted trajectory)
-            weighted_rewards = (
-                collision_weight * collided
-                + goal_achieved_weight * goal_achieved
-                + off_road_weight * off_road
-            )
+        elif self.config.reward_type == "guided_autonomy":
 
-            agent_states = GlobalEgoState.from_tensor(
-                self.sim.absolute_self_observation_tensor(),
-                self.backend,
-                self.device,
+            self.base_rewards = (
+                collision_weight * collided + off_road_weight * off_road
             )
 
-            agent_pos = torch.stack(
-                [agent_states.pos_x, agent_states.pos_y], dim=-1
-            )
+            step_in_world = self.step_in_world[:, 0, :].squeeze(-1)
 
-            # Extract VBD positions at current time steps for each world
-            vbd_pos = []
-            for i in range(self.num_worlds):
-                current_time = (
-                    self.world_time_steps[i].item() - self.init_steps
-                )
-                # Make sure we don't exceed trajectory length
-                current_time = min(
-                    current_time, self.vbd_trajectories.shape[2] - 1
-                )
-                vbd_pos.append(self.vbd_trajectories[i, :, current_time, :2])
-            vbd_pos_tensor = torch.stack(vbd_pos)
+            # Assumption: All worlds are at the same time step
+            # Check if we still have referene trajectory points, if not
+            # we set the guidance errors to zero.
+            if step_in_world[0] < self.reference_traj_len:
+                batch_indices = torch.arange(step_in_world.shape[0])
 
-            # Compute euclidean distance between agent and logs
-            dist_to_vbd = torch.norm(vbd_pos_tensor - agent_pos, dim=-1)
+                # Guidance
+                suggested_pos_xy = self.reference_trajectory.pos_xy[
+                    batch_indices, :, step_in_world, :
+                ]
 
-            # Add reward based on inverse distance to logs
-            weighted_rewards += self.vbd_trajectory_weight * torch.exp(
-                -dist_to_vbd
-            )
+                suggested_speed = self.reference_trajectory.ref_speed[
+                    batch_indices, :, step_in_world
+                ].squeeze(-1)
 
-            return weighted_rewards
+                suggested_heading = self.reference_trajectory.yaw[
+                    batch_indices, :, step_in_world
+                ].squeeze(-1)
 
-        elif self.config.reward_type == "follow_waypoints":
-            # Reward based on minimizing distance to time-aligned waypoints plus penalty for collision/off-road
-            self.base_rewards = (
-                goal_achieved_weight * goal_achieved
-                + collision_weight * collided
-                + off_road_weight * off_road
-            )
+                is_valid = (
+                    self.reference_trajectory.valids[
+                        batch_indices, :, step_in_world
+                    ]
+                    .squeeze(-1)
+                    .bool()
+                )
 
-            # Extract waypoints (ground truth) at time t
-            step_in_world = self.step_in_world[:, 0, :].squeeze(-1)
-            batch_indices = torch.arange(step_in_world.shape[0])
-            gt_agent_pos = self.log_trajectory.pos_xy[
-                batch_indices, :, step_in_world, :
-            ]
+                # Get actual agent positions
+                agent_states = GlobalEgoState.from_tensor(
+                    self.sim.absolute_self_observation_tensor(),
+                    self.backend,
+                    self.device,
+                )
 
-            gt_agent_speed = self.log_trajectory.ref_speed[
-                batch_indices, :, step_in_world
-            ]
-            valid_mask = (
-                self.log_trajectory.valids[batch_indices, :, step_in_world]
-                .squeeze(-1)
-                .bool()
-            )
+                actual_agent_pos_xy = agent_states.pos_xy
 
-            # Get actual agent positions
-            agent_state = GlobalEgoState.from_tensor(
-                self.sim.absolute_self_observation_tensor(),
-                self.backend,
-                self.device,
-            )
+                actual_agent_speed = (
+                    self.sim.self_observation_tensor().to_torch()[:, :, 0]
+                )
 
-            actual_agent_speed = self.sim.self_observation_tensor().to_torch()[
-                :, :, 0
-            ]
+                actual_agent_heading = agent_states.rotation_angle
 
-            actual_agent_pos = torch.stack(
-                [agent_state.pos_x, agent_state.pos_y], dim=-1
-            )
+                # Compute distances
+                guidance_pos_error = torch.norm(
+                    suggested_pos_xy - actual_agent_pos_xy, dim=-1
+                )
+                guidance_speed_error = (
+                    suggested_speed - actual_agent_speed
+                ) ** 2
+                guidance_heading_error = (
+                    suggested_heading - actual_agent_heading
+                ) ** 2
+
+                self.guidance_error = (
+                    -self.config.guidance_pos_xy_weight
+                    * torch.log(guidance_pos_error + 1.0)
+                    - self.config.guidance_speed_weight
+                    * torch.log(guidance_speed_error + 1.0)
+                    - self.config.guidance_heading_weight
+                    * torch.log(guidance_heading_error + 1.0)
+                )
 
-            speed_error = (gt_agent_speed - actual_agent_speed) ** 2
+                # Zero-out guidance errors for invalid time steps, that is,
+                # those that were not observed at the current time step
+                self.guidance_error[~is_valid] = 0.0
+
+                # Reduce guidance density
+                if self.config.guidance_sample_interval > 1:
+                    waypoint_mask = (
+                        (
+                            step_in_world
+                            % self.config.guidance_sample_interval
+                            == 0
+                        )
+                        .float()
+                        .unsqueeze(1)
+                    )
+                    self.guidance_error = self.guidance_error * waypoint_mask
 
-            # Compute euclidean distance between agent and waypoints
-            dist_to_waypoints = torch.norm(
-                gt_agent_pos - actual_agent_pos, dim=-1
-            )
+            else:
+                self.guidance_error = torch.zeros_like(self.base_rewards)
 
-            # Penalty for jerky movements
+            # Encourage smooth driving
             if hasattr(self, "action_diff"):
                 acceleration_jerk = (
                     self.action_diff[:, :, 0] ** 2
@@ -625,37 +623,16 @@ def get_rewards(
                 )  # Second action component is steering
 
                 self.smoothness_penalty = -(
-                    self.config.jerk_smoothness_scale * acceleration_jerk
-                    + self.config.jerk_smoothness_scale * steering_jerk
+                    self.config.smoothness_weight * acceleration_jerk
+                    + self.config.smoothness_weight * steering_jerk
                 )
             else:
                 self.smoothness_penalty = torch.zeros_like(self.base_rewards)
 
-            self.distance_penalty = (
-                -self.config.waypoint_distance_scale
-                * torch.log(dist_to_waypoints + 1.0)
-                - self.config.speed_distance_scale
-                * torch.log(speed_error + 1.0)
-            )
-
-            # Zero-out distance penalty for invalid time steps, that is,
-            # The reference positions have not been observed at every time step
-            # if not observed, we set the distance penalty to 0
-            self.distance_penalty[~valid_mask] = 0.0
+            self.guidance_error += self.smoothness_penalty
 
-            self.distance_penalty += self.smoothness_penalty
-
-            # Apply waypoint mask only if sampling interval is greater than 1
-            if self.config.waypoint_sample_interval > 1:
-                waypoint_mask = (
-                    (step_in_world % self.config.waypoint_sample_interval == 0)
-                    .float()
-                    .unsqueeze(1)
-                )
-                self.distance_penalty = self.distance_penalty * waypoint_mask
-
-            # Combine base rewards with distance penalty
-            rewards = self.base_rewards + self.distance_penalty
+            # Combine base rewards with guidance error
+            rewards = self.base_rewards + self.guidance_error
 
             return rewards
 
@@ -707,7 +684,11 @@ def _apply_actions(self, actions):
                 self.action_value_tensor.clone()
             )
 
-        if self.config.dynamics_model == "state" and self.previous_action_value_tensor.shape != self.action_value_tensor.shape:
+        if (
+            self.config.dynamics_model == "state"
+            and self.previous_action_value_tensor.shape
+            != self.action_value_tensor.shape
+        ):
             self.previous_action_value_tensor = (
                 self.action_value_tensor.clone()
             )
@@ -831,58 +812,57 @@ def _set_continuous_action_space(self) -> None:
         )
         return action_space
 
-    def _get_ego_state(self, mask=None) -> torch.Tensor:
-        """Get the ego state."""
+    def _get_guidance(self, mask=None) -> torch.Tensor:
+        """Receive (expert) suggestions from pre-trained model or logs."""
 
-        if not self.config.ego_state:
-            return torch.Tensor().to(self.device)
-
-        ego_state = LocalEgoState.from_tensor(
-            self_obs_tensor=self.sim.self_observation_tensor(),
-            backend=self.backend,
-            device=self.device,
-            mask=mask,
-        )
+        if not self.config.guidance:
+            return torch.zeros(0, device=self.device)
 
-        if self.config.norm_obs:
-            ego_state.normalize()
-
-        base_fields = [
-            ego_state.speed.unsqueeze(-1),
-            ego_state.vehicle_length.unsqueeze(-1),
-            ego_state.vehicle_width.unsqueeze(-1),
-            ego_state.is_collided.unsqueeze(-1),
-        ]
+        guidance = []
 
         if mask is None:
 
-            base_fields.append(
-                self.previous_action_value_tensor[:, :, :2]
-                / constants.MAX_ACTION_VALUE,  # Previous accel, steering
+            valid_timesteps_mask = self.reference_trajectory.valids.bool()
+
+            # Provide agent with index to pay attention to through one-hot encoding
+            next_step_in_world = torch.clamp(
+                self.step_in_world[:, 0, :].squeeze(-1) + 1,
+                min=0,
+                max=self.reference_traj_len - 1,
             )
+            time_one_hot = torch.zeros(
+                (
+                    self.num_worlds,
+                    self.max_agent_count,
+                    self.reference_traj_len,
+                    1,
+                ),
+                device=self.device,
+            )
+            time_one_hot[:, :, next_step_in_world, :] = 1.0
 
             if self.config.add_reference_speed:
-
-                avg_ref_speed = (
-                    self.log_trajectory.ref_speed.clone().mean(axis=-1)
+                reference_speed = (
+                    self.reference_trajectory.ref_speed.clone()
                     / constants.MAX_SPEED
                 )
+                reference_speed[~valid_timesteps_mask] = constants.INVALID_ID
+                guidance.append(reference_speed)
 
-                base_fields.append(avg_ref_speed.unsqueeze(-1))
-
-            if self.config.add_reference_path:
-
-                state = (
-                    self.sim.absolute_self_observation_tensor()
-                    .to_torch()
-                    .clone().to(self.device)
+            states = None
+            if (
+                self.config.add_reference_pos_xy
+                or self.config.add_reference_heading
+            ):
+                states = GlobalEgoState.from_tensor(
+                    self.sim.absolute_self_observation_tensor(),
+                    self.backend,
+                    self.device,
                 )
-                global_ego_pos_xy = state[:, :, :2]
-                global_ego_yaw = state[:, :, 7]
-                glob_reference_xy = self.log_trajectory.pos_xy
-                agent_indices = torch.arange(self.max_cont_agents)
+
+            if self.config.add_reference_pos_xy:
+                glob_reference_xy = self.reference_trajectory.pos_xy
                 local_reference_xy = torch.empty_like(glob_reference_xy)
-                valid_timesteps_mask = self.log_trajectory.valids.bool()
 
                 # Transform reference path to be relative to current
                 # agent positions and heading
@@ -894,8 +874,10 @@ def _get_ego_state(self, mask=None) -> torch.Tensor:
                             global_pos_xy=glob_reference_xy[
                                 world_idx, agent_idx, :, :
                             ],
-                            ego_pos=global_ego_pos_xy[world_idx, agent_idx],
-                            ego_yaw=global_ego_yaw[world_idx, agent_idx],
+                            ego_pos=states.pos_xy[world_idx, agent_idx],
+                            ego_yaw=states.rotation_angle[
+                                world_idx, agent_idx
+                            ],
                             device=self.device,
                         )
 
@@ -909,23 +891,6 @@ def _get_ego_state(self, mask=None) -> torch.Tensor:
                     ~valid_timesteps_mask.expand_as(local_reference_xy)
                 ] = constants.INVALID_ID
 
-                # Provide agent with index to pay attention to through one-hot encoding
-                next_step_in_world = torch.clamp(
-                    self.step_in_world[:, 0, :].squeeze(-1) + 1,
-                    min=0,
-                    max=self.episode_len,
-                )
-                time_one_hot = torch.zeros(
-                    (
-                        self.num_worlds,
-                        self.max_agent_count,
-                        self.reference_path_length,
-                        1,
-                    ),
-                    device=self.device,
-                )
-                time_one_hot[:, :, next_step_in_world, :] = 1.0
-
                 # Make unnormalized reference path available for plotting
                 self.reference_path = torch.cat(
                     (local_ref_xy_orig, time_one_hot), dim=-1
@@ -934,100 +899,91 @@ def _get_ego_state(self, mask=None) -> torch.Tensor:
                 reference_path = torch.cat(
                     (local_reference_xy, time_one_hot), dim=-1
                 )
+                guidance.append(reference_path)
 
-                # Flatten the dimensions for stacking
-                base_fields.append(reference_path.flatten(start_dim=2))
-
-                # batch_size = local_reference_xy.shape[0]
-                # num_points = local_reference_xy.shape[1]
-                # time_steps = local_reference_xy.shape[2]
-
-                # Create dropout mask for the time dimension
-                # Shape: [batch_size, num_points, time_steps, 1]
-                # point_dropout_mask = torch.bernoulli(
-                #     torch.ones(
-                #         batch_size,
-                #         num_points,
-                #         time_steps,
-                #         1,
-                #         device=local_reference_xy.device,
-                #     )
-                #     * (1 - self.config.prob_reference_dropout)
-                # ).bool()
-
-                # Apply dropout mask
-                # self.local_reference_xy = (
-                #     local_reference_xy * point_dropout_mask
-                # )
+            if self.config.add_reference_heading:
+                reference_headings = self.reference_trajectory.yaw.clone()
 
-            if self.config.reward_type == "reward_conditioned":
+                # Transform headings to local coordinate frame
+                for world_idx in range(self.num_worlds):
+                    for agent_idx in range(self.max_cont_agents):
+                        # Subtract current agent heading to get relative heading
+                        reference_headings[
+                            world_idx, agent_idx
+                        ] -= states.rotation_angle[world_idx, agent_idx]
 
-                # Create expanded weights for all environments
-                # Expand from [max_agents, 3] to [num_worlds, max_agents]
-                collision_weights = self.reward_weights_tensor[:, 0].expand(
-                    self.num_worlds, -1
-                )
-                goal_weights = self.reward_weights_tensor[:, 1].expand(
-                    self.num_worlds, -1
-                )
-                off_road_weights = self.reward_weights_tensor[:, 2].expand(
-                    self.num_worlds, -1
+                # Normalize
+                reference_headings = (
+                    reference_headings / constants.MAX_ORIENTATION_RAD
                 )
 
-                full_fields = base_fields + [
-                    collision_weights,
-                    goal_weights,
-                    off_road_weights,
-                ]
-                return torch.stack(full_fields).permute(1, 2, 0)
-            else:
-                return torch.cat(base_fields, dim=-1)
-        else:
+                # Set invalid timesteps to -1
+                reference_headings[
+                    ~valid_timesteps_mask
+                ] = constants.INVALID_ID
+                guidance.append(reference_headings)
 
-            base_fields.append(
-                self.previous_action_value_tensor[mask][:, :2]
-                / constants.MAX_ACTION_VALUE,  # Previous accel, steering
+            return torch.cat(guidance, dim=-1).flatten(start_dim=2)
+
+        else:
+            batch_size = mask.sum()
+            batch_indices = torch.arange(batch_size)
+
+            # Provide agent with index to pay attention to through one-hot encoding
+            next_step_in_world = torch.clamp(
+                self.step_in_world[mask] + 1,
+                min=0,
+                max=self.reference_traj_len - 1,
+            )
+            time_one_hot = torch.zeros(
+                (batch_size, self.reference_traj_len, 1),
+                device=self.device,
             )
+            time_one_hot[batch_indices, next_step_in_world] = 1.0
+
+            valid_timesteps_mask = self.reference_trajectory.valids.bool()[
+                mask
+            ]
+
+            states = None
+            if (
+                self.config.add_reference_pos_xy
+                or self.config.add_reference_heading
+            ):
+                states = GlobalEgoState.from_tensor(
+                    self.sim.absolute_self_observation_tensor(),
+                    self.backend,
+                    self.device,
+                )
 
             if self.config.add_reference_speed:
-                avg_ref_speed = (
-                    self.log_trajectory.ref_speed[mask].clone().mean(axis=-1)
+                reference_speed = (
+                    self.reference_trajectory.ref_speed[mask].clone()
                     / constants.MAX_SPEED
                 )
-                base_fields.append(avg_ref_speed.unsqueeze(-1))
-
-            if self.config.add_reference_path:
-
-                # State information
-                state = (
-                    self.sim.absolute_self_observation_tensor()
-                    .to_torch()
-                    .clone()[mask]
-                ).to(self.device)
-                global_ego_pos_xy = state[:, :2]  # Shape: [batch, 2]
-                global_ego_yaw = state[:, 7]  # Shape: [batch]
-                global_reference_xy = self.log_trajectory.pos_xy.clone()[mask]
-                valid_timesteps_mask = self.log_trajectory.valids.bool()[mask]
-                batch_size = global_reference_xy.shape[0]
-                batch_indices = torch.arange(batch_size)
+                reference_speed[~valid_timesteps_mask] = constants.INVALID_ID
+                guidance.append(reference_speed)
 
-                # Translate all points to a local coordinate frame
-                translated = global_reference_xy - global_ego_pos_xy.unsqueeze(
-                    1
-                )
+            if self.config.add_reference_pos_xy:
+                global_reference_xy = self.reference_trajectory.pos_xy.clone()[
+                    mask
+                ]
 
-                # Create rotation matrices for all agents at once
-                cos_yaw = torch.cos(global_ego_yaw)
-                sin_yaw = torch.sin(global_ego_yaw)
+                # Translate all points to a local coordinate frame
+                translated = global_reference_xy - states.pos_xy[
+                    mask
+                ].unsqueeze(1)
 
                 # Create batch of rotation matrices: [batch, 2, 2]
+                cos_yaw = torch.cos(states.rotation_angle[mask])
+                sin_yaw = torch.sin(states.rotation_angle[mask])
                 rotation_matrices = torch.stack(
                     [
                         torch.stack([cos_yaw, sin_yaw], dim=1),
                         torch.stack([-sin_yaw, cos_yaw], dim=1),
                     ],
                     dim=1,
-                )  # Shape: [batch, 2, 2]
+                )
 
                 # Apply rotation to all points
                 local_reference_xy = torch.bmm(
@@ -1044,16 +1000,6 @@ def _get_ego_state(self, mask=None) -> torch.Tensor:
                     ~valid_timesteps_mask.expand_as(local_reference_xy)
                 ] = constants.INVALID_ID
 
-                # Provide agent with index to pay attention to through one-hot encoding
-                next_step_in_world = torch.clamp(
-                    self.step_in_world[mask] + 1, min=0, max=self.episode_len
-                )
-                time_one_hot = torch.zeros(
-                    (batch_size, self.reference_path_length, 1),
-                    device=self.device,
-                )
-                time_one_hot[batch_indices, next_step_in_world] = 1.0
-
                 # Stack
                 reference_path = torch.cat(
                     (local_reference_xy, time_one_hot), dim=2
@@ -1062,15 +1008,80 @@ def _get_ego_state(self, mask=None) -> torch.Tensor:
                 self.reference_path = torch.cat(
                     (local_reference_xy_orig, time_one_hot), dim=2
                 )
+                guidance.append(reference_path)
 
-                # Stack
-                base_fields.append(reference_path.flatten(start_dim=1))
+            if self.config.add_reference_heading:
+                reference_headings = self.reference_trajectory.yaw[
+                    mask
+                ].clone()
+
+                # Translate headings to local coordinate frame by subtracting current global agent headings
+                reference_headings = (
+                    reference_headings
+                    - states.rotation_angle[mask].view(-1, 1, 1)
+                )
+
+                # Normalize by 2pi to ensure values are in [-1, 1]
+                reference_headings = (
+                    reference_headings / constants.MAX_ORIENTATION_RAD
+                )
+
+                # Set invalid timesteps to -1
+                reference_headings[
+                    ~valid_timesteps_mask
+                ] = constants.INVALID_ID
+                guidance.append(reference_headings)
+
+        return torch.cat(guidance, dim=-1).flatten(start_dim=1)
+
+    def _get_ego_state(self, mask=None) -> torch.Tensor:
+        """Get the ego state."""
+
+        if not self.config.ego_state:
+            return torch.Tensor().to(self.device)
+
+        ego_state = LocalEgoState.from_tensor(
+            self_obs_tensor=self.sim.self_observation_tensor(),
+            backend=self.backend,
+            device=self.device,
+            mask=mask,
+        )
+
+        if self.config.norm_obs:
+            ego_state.normalize()
+
+        base_fields = [
+            ego_state.speed.unsqueeze(-1),
+            ego_state.vehicle_length.unsqueeze(-1),
+            ego_state.vehicle_width.unsqueeze(-1),
+            ego_state.is_collided.unsqueeze(-1),
+        ]
+
+        if mask is None:
+
+            if self.config.add_previous_action:
+                normalized_prev_actions = (
+                    self.previous_action_value_tensor[:, :, :2]
+                    / constants.MAX_ACTION_VALUE
+                )
+                base_fields.append(normalized_prev_actions)
 
             if self.config.reward_type == "reward_conditioned":
-                # For masked agents, we need to extract agent indices from the mask
-                world_indices, agent_indices = torch.where(mask)
 
-                # Get the reward weights for these specific agents
+                full_fields = base_fields + [
+                    self.reward_weights_tensor.expand(self.num_worlds, -1)
+                ]
+                return torch.stack(full_fields).permute(1, 2, 0)
+            else:
+                return torch.cat(base_fields, dim=-1)
+        else:
+
+            base_fields.append(
+                self.previous_action_value_tensor[mask][:, :2]
+                / constants.MAX_ACTION_VALUE,  # Previous accel, steering
+            )
+            if self.config.reward_type == "reward_conditioned":
+                _, agent_indices = torch.where(mask)
                 weights_for_masked_agents = self.reward_weights_tensor.to(
                     self.device
                 )[agent_indices]
@@ -1437,12 +1448,12 @@ def get_obs(self, mask=None):
         Returns:
             torch.Tensor: (num_worlds, max_agent_count, num_features)
         """
-        # Base observations
         ego_states = self._get_ego_state(mask)
         partner_observations = self._get_partner_obs(mask)
         road_map_observations = self._get_road_map_obs(mask)
+        guidance = self._get_guidance(mask)
 
-        if self.use_vbd and self.config.vbd_in_obs:
+        if self.config.use_vbd and self.config.vbd_in_obs:
             # Add ego-centric VBD trajectories
             vbd_observations = self._get_vbd_obs(mask)
 
@@ -1461,6 +1472,7 @@ def get_obs(self, mask=None):
                     ego_states,
                     partner_observations,
                     road_map_observations,
+                    guidance,
                 ),
                 dim=-1,
             )
@@ -1546,7 +1558,10 @@ def remove_agents_by_id(
         )
 
         # Reset static scenario data for the visualizer
-        self.vis.initialize_static_scenario_data(self.cont_agent_mask)
+        self.vis.initialize_static_scenario_data(
+            controlled_agent_mask=self.cont_agent_mask,
+            reference_trajectory=self.reference_trajectory,
+        )
 
     def swap_data_batch(self, data_batch=None):
         """
@@ -1576,36 +1591,15 @@ def swap_data_batch(self, data_batch=None):
             self.cont_agent_mask.sum().item()
         )
 
-        # Load VBD trajectories for the new batch if VBD is enabled
-        if self.use_vbd:
-            self._load_vbd_trajectories()
+        # Receive guidance trajectories from the new batch of scenarios
+        self.setup_guidance()
 
         # Reset static scenario data for the visualizer
-        self.vis.initialize_static_scenario_data(self.cont_agent_mask)
-
-        # Obtain new log trajectory
-        self.log_trajectory = LogTrajectory.from_tensor(
-            self.sim.expert_trajectory_tensor(),
-            self.num_worlds,
-            self.max_agent_count,
-            backend=self.backend,
-            device=self.device
-        )
-
-    def _load_vbd_trajectories(self):
-        """Load VBD trajectories directly from the simulator."""
-        if not self.use_vbd:
-            return
-
-        # Get VBD trajectories from the simulator
-        vbd_traj = VBDTrajectory.from_tensor(
-            self.sim.vbd_trajectory_tensor(),
-            backend=self.backend,
-            device=self.device,
+        self.vis.initialize_static_scenario_data(
+            controlled_agent_mask=self.cont_agent_mask,
+            reference_trajectory=self.reference_trajectory,
         )
 
-        self.vbd_trajectories = vbd_traj
-
     def get_expert_actions(self):
         """Get expert actions for the full trajectories across worlds.
 
@@ -1621,7 +1615,7 @@ def get_expert_actions(self):
             self.num_worlds,
             self.max_agent_count,
             backend=self.backend,
-            device=self.device
+            device=self.device,
         )
 
         if self.config.dynamics_model == "delta_local":
@@ -1704,11 +1698,12 @@ def get_scenario_ids(self):
 if __name__ == "__main__":
 
     env_config = EnvConfig(
-        dynamics_model="delta_local",
-        reward_type="follow_waypoints",
-        add_reference_path=True,
-        init_mode="womd_tracks_to_predict",
-        init_steps=10,       
+        guidance=True,
+        guidance_mode="log_replay",  # "vbd_amortized"
+        add_reference_pos_xy=True,
+        add_reference_speed=True,
+        add_reference_heading=True,
+        reward_type="guided_autonomy",
     )
     render_config = RenderConfig()
 
@@ -1719,7 +1714,7 @@ def get_scenario_ids(self):
         dataset_size=1,
         sample_with_replacement=False,
         shuffle=False,
-        file_prefix=""
+        file_prefix="",
     )
 
     # Make env
@@ -1731,6 +1726,7 @@ def get_scenario_ids(self):
     )
 
     control_mask = env.cont_agent_mask
+
     print(f"Number of controlled agents: {control_mask.sum()}")
 
     # Rollout
@@ -1742,7 +1738,7 @@ def get_scenario_ids(self):
     expert_actions, _, _, _ = env.get_expert_actions()
 
     env_idx = 0
-    
+
     highlight_agent = torch.where(control_mask[env_idx, :])[0][0].item()
 
     agent_positions = []
@@ -1750,12 +1746,8 @@ def get_scenario_ids(self):
         env.sim.absolute_self_observation_tensor(),
         device=env.device,
     )
-    means_xy = (
-            env.sim.world_means_tensor().to_torch()[:, :2].to(env.device)
-        )
-    init_state.restore_mean(
-        mean_x=means_xy[:, 0], mean_y=means_xy[:, 1]
-    )
+    means_xy = env.sim.world_means_tensor().to_torch()[:, :2].to(env.device)
+    init_state.restore_mean(mean_x=means_xy[:, 0], mean_y=means_xy[:, 1])
     agent_positions.append(init_state.pos_xy[env_idx, highlight_agent])
 
     print(f"Highlighted agent: {highlight_agent}")
@@ -1783,7 +1775,7 @@ def get_scenario_ids(self):
             zoom_radius=70,
             time_steps=[t],
             center_agent_indices=[highlight_agent],
-            plot_waypoints=True,
+            plot_guidance_pos_xy=True,
         )
 
         agent_obs = env.vis.plot_agent_observation(
@@ -1798,10 +1790,6 @@ def get_scenario_ids(self):
         sim_frames.append(img_from_fig(sim_states[0]))
         agent_obs_frames.append(img_from_fig(agent_obs))
 
-        world_time_steps = (
-            torch.Tensor([t]).repeat((1, env.num_worlds)).long().to(env.device)
-        )
-
         obs = env.get_obs(control_mask)
         reward = env.get_rewards()
 
diff --git a/gpudrive/integrations/puffer/ppo.py b/gpudrive/integrations/puffer/ppo.py
index 3bf2ab1f9..70cad1461 100644
--- a/gpudrive/integrations/puffer/ppo.py
+++ b/gpudrive/integrations/puffer/ppo.py
@@ -672,6 +672,12 @@ def flatten_batch(self, advantages_np):
         self.b_values = self.b_values[b_flat]
         self.b_returns = self.b_advantages + self.b_values
 
+        if self.b_obs.max() > 1.0 or self.b_obs.min() < -1.0:
+            print(
+                f"Warning: The batch of observations contains features outside the range [-1, 1]."
+                f"Please check your observation normalization; min {self.b_obs.min()}, max {self.b_obs.max()}"
+            )
+
 
 class Utilization(Thread):
     def __init__(self, delay=1, maxlen=20):
diff --git a/gpudrive/networks/agents.py b/gpudrive/networks/agents.py
index 23f5ba88d..565374a35 100644
--- a/gpudrive/networks/agents.py
+++ b/gpudrive/networks/agents.py
@@ -40,26 +40,38 @@ def __init__(
         self.action_dim = action_dim
         self.top_k = top_k
 
-        # Indices for unpacking the observation modalities
-        self.ego_state_idx = (
-            9
-            if self.config["reward_type"] == "reward_conditioned"
-            else constants.EGO_FEAT_DIM
-        )
-        if self.config[
-            "add_reference_path"
-        ]:  # Every agent receives a reference path
-            # NOTE: Hardcoded to 91 for now
-            self.ego_state_idx += 91 * 3
+        # Indices for unpacking the different observation modalities
+        self.ego_state_idx = constants.EGO_FEAT_DIM
+        if self.config["reward_type"] == "reward_conditioned":
+            self.ego_state_idx += 3
+        if self.config["add_previous_action"]:
+            self.ego_state_idx += 2
 
-        if self.config["add_reference_speed"]:
-            self.ego_state_idx += 1
         self.max_controlled_agents = madrona_gpudrive.kMaxAgentCount
         self.max_observable_agents = self.max_controlled_agents - 1
         self.partner_obs_idx = self.ego_state_idx + (
             constants.PARTNER_FEAT_DIM * self.max_observable_agents
         )
 
+        self.road_map_idx = self.partner_obs_idx + (
+            constants.ROAD_GRAPH_TOP_K * constants.ROAD_GRAPH_FEAT_DIM
+        )
+
+        if self.config["guidance"]:
+            self.guidance_feature_dim = 0
+            # One-hot encoding signalling the next time step
+            self.guidance_feature_dim += constants.LOG_TRAJECTORY_LENGTH
+            if self.config["add_reference_pos_xy"]:
+                self.guidance_feature_dim += (
+                    constants.LOG_TRAJECTORY_LENGTH * 2
+                )
+            if self.config["add_reference_speed"]:
+                # TODO: Change to reference path-length (90) if using the vbd_amortized trajs
+                self.guidance_feature_dim += constants.LOG_TRAJECTORY_LENGTH
+            if self.config["add_reference_heading"]:
+                self.guidance_feature_dim += constants.LOG_TRAJECTORY_LENGTH
+            self.guidance_idx = self.road_map_idx + self.guidance_feature_dim
+
         # Shared embedding networks for both actor and critic
         self.ego_embed = nn.Sequential(
             layer_init(nn.Linear(self.ego_state_idx, embed_dim)),
@@ -85,9 +97,17 @@ def __init__(
             layer_init(nn.Linear(embed_dim, embed_dim)),
         )
 
+        self.guidance_embed = nn.Sequential(
+            layer_init(nn.Linear(self.guidance_feature_dim, embed_dim)),
+            nn.LayerNorm(embed_dim),
+            self.act_func,
+            nn.Dropout(self.dropout),
+            layer_init(nn.Linear(embed_dim, embed_dim)),
+        )
+
         # Critic network
         self.critic = nn.Sequential(
-            layer_init(nn.Linear((2 * top_k + 1) * embed_dim, 32)),
+            layer_init(nn.Linear((2 * top_k + 2) * embed_dim, 32)),
             nn.LayerNorm(32),
             self.act_func,
             layer_init(nn.Linear(32, 1), std=1.0),
@@ -95,7 +115,7 @@ def __init__(
 
         # Actor network
         self.actor = nn.Sequential(
-            layer_init(nn.Linear((2 * top_k + 1) * embed_dim, 64)),
+            layer_init(nn.Linear((2 * top_k + 2) * embed_dim, 64)),
             nn.LayerNorm(64),
             self.act_func,
             layer_init(nn.Linear(64, action_dim), std=0.01),
@@ -109,12 +129,13 @@ def forward(self, x, action=None):
                 If None, a new actions are sampled.
         """
         # Unpack into modalities
-        ego_state, partner_obs, road_graph = self.unpack_obs(x)
+        ego_state, partner_obs, road_graph, guidance = self.unpack_obs(x)
 
         # Use shared embedding networks for both actor and critic
         ego_embed = self.ego_embed(ego_state)
         partner_embed = self.partner_embed(partner_obs)
         road_embed = self.road_map_embed(road_graph)
+        guidance_embed = self.guidance_embed(guidance)
 
         # Take top k features from partner and road embeddings
         partner_max_pool = torch.topk(partner_embed, k=self.top_k, dim=1)[
@@ -127,7 +148,10 @@ def forward(self, x, action=None):
         )
 
         # Concatenate the embeddings
-        z = torch.cat([ego_embed, partner_max_pool, road_max_pool], dim=-1)
+        z = torch.cat(
+            [ego_embed, partner_max_pool, road_max_pool, guidance_embed],
+            dim=-1,
+        )
 
         # Pass to the actor and critic networks
         logits = self.actor(z)
@@ -151,7 +175,8 @@ def unpack_obs(self, obs_flat):
 
         ego_state = obs_flat[:, : self.ego_state_idx]
         partner_obs = obs_flat[:, self.ego_state_idx : self.partner_obs_idx]
-        roadgraph_obs = obs_flat[:, self.partner_obs_idx :]
+        roadgraph_obs = obs_flat[:, self.partner_obs_idx : self.road_map_idx]
+        guidance = obs_flat[:, self.road_map_idx :]
 
         road_objects = partner_obs.view(
             -1, self.max_observable_agents, constants.PARTNER_FEAT_DIM
@@ -160,7 +185,7 @@ def unpack_obs(self, obs_flat):
             -1, TOP_K_ROAD_POINTS, constants.ROAD_GRAPH_FEAT_DIM
         )
 
-        return ego_state, road_objects, road_graph
+        return ego_state, road_objects, road_graph, guidance
 
 
 class SeparateActorCriticAgent(nn.Module):
@@ -191,7 +216,7 @@ def __init__(
             else constants.EGO_FEAT_DIM
         )
         if self.config[
-            "add_reference_path"
+            "add_reference_pos_xy"
         ]:  # Every agent receives a reference path
             self.ego_state_idx += 91 * 2
 
diff --git a/gpudrive/networks/late_fusion.py b/gpudrive/networks/late_fusion.py
index ccda595d8..98b18fc57 100644
--- a/gpudrive/networks/late_fusion.py
+++ b/gpudrive/networks/late_fusion.py
@@ -107,7 +107,7 @@ def __init__(
                     # Agents know their "type", consisting of three weights
                     # that determine the reward (collision, goal, off-road)
                     self.ego_state_idx += 3
-            if "add_reference_path" in self.config:
+            if "add_reference_pos_xy" in self.config:
                 self.ego_state_idx += 2 * 91
 
             self.vbd_in_obs = self.config.vbd_in_obs
diff --git a/gpudrive/utils/generate_sbatch.py b/gpudrive/utils/generate_sbatch.py
index d4c61e975..d81078a58 100644
--- a/gpudrive/utils/generate_sbatch.py
+++ b/gpudrive/utils/generate_sbatch.py
@@ -243,30 +243,33 @@ def save_script(filename, file_path, fields, params, param_order=None):
 
 if __name__ == "__main__":
 
-    group = "minimal_tiny"
+    group = "wosac_scale_100_v3"
 
     fields = {
-        "time_h": 6,  # Max time per job (job will finish if run is done before)
+        "time_h": 47,  # Max time per job (job will finish if run is done before)
         "num_gpus": 1,  # GPUs per job
         "max_sim_jobs": 30,  # Max jobs at the same time
         "memory": 70,
         "job_name": group,
-        "run_file": "baselines/ppo/ppo_waypoint.py",
+        "run_file": "baselines/ppo/ppo_guided_autonomy.py",
     }
-    
+
     hyperparams = {
-        "group": [group], # Group name
-        "num_worlds": [500],
-        "resample_scenes": [0], 
-        "k_unique_scenes": [4],
-        #"resample_interval": [5_000_000],
-        #"resample_dataset_size": [10_000],
-        #"total_timesteps": [3_000_000_000],
+        "group": [group],  # Group name
+        "num_worlds": [700],
+        "resample_scenes": [0],
+        "k_unique_scenes": [100],
+        # "resample_interval": [5_000_000],
+        # "resample_dataset_size": [10_000],
+        # "total_timesteps": [3_000_000_000],
         "batch_size": [262_144],
         "minibatch_size": [16_384],
-        "waypoint_distance_scale": [0.0, 0.05, 0.1],
-        "ent_coef": [0.001],
+        # "guidance_pos_xy_weight": [0.01],
+        # "guidance_speed_weight": [0.01],
+        # "guidance_heading_weight": [0.01],
+        "ent_coef": [0.005, 0.001, 0.003, 0.01],
         "vf_coef": [0.5],
+        "update_epochs": [4],
         "render": [0],
     }
 
diff --git a/gpudrive/visualize/core.py b/gpudrive/visualize/core.py
index 02545c92f..c51a8677d 100644
--- a/gpudrive/visualize/core.py
+++ b/gpudrive/visualize/core.py
@@ -41,8 +41,8 @@ def __init__(
         self,
         sim_object,
         controlled_agent_mask,
+        reference_trajectory,
         goal_radius,
-        backend: str,
         num_worlds: int,
         render_config: Dict[str, Any],
         env_config: Dict[str, Any],
@@ -56,12 +56,17 @@ def __init__(
         self.figsize = (15, 15)
         self.env_config = env_config
         self.render_3d = render_config.render_3d
-        self.vehicle_height = (
-            render_config.vehicle_height
-        )  # Default vehicle height
-        self.initialize_static_scenario_data(controlled_agent_mask)
+        self.vehicle_height = render_config.vehicle_height
+        self.initialize_static_scenario_data(
+            controlled_agent_mask=controlled_agent_mask,
+            reference_trajectory=reference_trajectory,
+        )
 
-    def initialize_static_scenario_data(self, controlled_agent_mask):
+    def initialize_static_scenario_data(
+        self,
+        controlled_agent_mask,
+        reference_trajectory,
+    ):
         """
         Initialize key information for visualization based on the
         current batch of scenarios.
@@ -76,28 +81,16 @@ def initialize_static_scenario_data(self, controlled_agent_mask):
             backend=self.backend,
             device=self.device,
         )
-        self.controlled_agent_mask = controlled_agent_mask
+        self.controlled_agent_mask = controlled_agent_mask.clone().to(
+            self.device
+        )
 
         if isinstance(controlled_agent_mask, ArrayImpl):
             self.controlled_agent_mask = torch.from_numpy(
                 np.array(controlled_agent_mask)
             )
 
-        self.controlled_agent_mask = self.controlled_agent_mask.to(self.device)
-
-        self.log_trajectory = LogTrajectory.from_tensor(
-            self.sim_object.expert_trajectory_tensor(),
-            self.num_worlds,
-            self.controlled_agent_mask.shape[1],
-            backend=self.backend,
-            device=self.device,
-        )
-
-        self.vbd_trajectory = VBDTrajectory.from_tensor(
-            self.sim_object.vbd_trajectory_tensor(),
-            backend=self.backend,
-            device=self.device,
-        )
+        self.trajectory = reference_trajectory
 
     def plot_simulator_state(
         self,
@@ -105,8 +98,7 @@ def plot_simulator_state(
         time_steps: Optional[List[int]] = None,
         center_agent_indices: Optional[List[int]] = None,
         zoom_radius: int = 100,
-        plot_waypoints: bool = False,
-        plot_vbd_trajectory: bool = False,
+        plot_guidance_pos_xy: bool = False,
         agent_positions: Optional[torch.Tensor] = None,
         backward_goals: bool = False,
         policy_masks: Optional[Dict[int, Dict[str, torch.Tensor]]] = None,
@@ -126,7 +118,7 @@ def plot_simulator_state(
             center_agent_indices: Optional list of center agent indices for zooming.
             figsize: Tuple for figure size of each subplot.
             zoom_radius: Radius for zooming in around the center agent.
-            plot_waypoints: If True, plots the waypoints from the human replays.
+            plot_guidance_pos_xy: If True, plots the waypoints from the human replays.
             agent_positions: Optional tensor to plot rolled out agent positions.
             backward_goals: If True, plots backward goals for controlled agents.
             policy_mask: dict
@@ -257,7 +249,6 @@ def plot_simulator_state(
         line_width_scale = max(self.figsize) / 15
 
         if policy_masks:
-
             world_based_policy_mask = {}
 
             for policy_name, (fn, mask) in policy_masks.items():
@@ -265,7 +256,6 @@ def plot_simulator_state(
                     if world not in world_based_policy_mask:
                         world_based_policy_mask[world] = {}
                     world_based_policy_mask[world][policy_name] = mask[world]
-
         else:
             world_based_policy_mask = None
 
@@ -273,19 +263,27 @@ def plot_simulator_state(
         for idx, (env_idx, time_step, center_agent_idx) in enumerate(
             zip(env_indices, time_steps, center_agent_indices)
         ):
+            # Create a completely new figure and axis for each environment to prevent carryover
+            plt.close(
+                "all"
+            )  # Close all existing figures first to prevent memory leaks
 
-            # Initialize figure and axes from cached road graph
-            fig, ax = plt.subplots(
-                figsize=self.figsize,
-                subplot_kw={"projection": "3d"} if self.render_3d else {},
-            )
+            # Initialize a new figure for each environment
+            fig = plt.figure(figsize=self.figsize)
+
+            # Create a new axis with proper projection
             if self.render_3d:
+                ax = fig.add_subplot(111, projection="3d")
                 ax.view_init(elev=30, azim=45)  # Set default 3D view angle
+            else:
+                ax = fig.add_subplot(111)
+
+            # Set up the figure and axis
             fig.subplots_adjust(left=0, right=1, bottom=0, top=1)
-            ax.clear()  # Clear any existing content
             ax.set_aspect("equal", adjustable="box")
-            figs.append(fig)  # Add the new figure
-            plt.close(fig)  # Close the figure to prevent carryover
+
+            # Add to figures list - use a copy to ensure it's detached from the current matplotlib state
+            figs.append(fig)
 
             # Get control mask and omit out-of-bound agents (dead agents)
             controlled = self.controlled_agent_mask[env_idx, :]
@@ -308,21 +306,12 @@ def plot_simulator_state(
                 marker_size_scale=marker_scale,
             )
 
-            if plot_waypoints:
-                self._plot_waypoints(
+            if plot_guidance_pos_xy:
+                self._plot_reference_xy(
                     ax=ax,
                     control_mask=controlled_live,
                     env_idx=env_idx,
-                    log_trajectory=self.log_trajectory,
-                    line_width_scale=line_width_scale,
-                )
-
-            if plot_vbd_trajectory:
-                self._plot_vbd_trajectory(
-                    ax=ax,
-                    control_mask=controlled_live,
-                    env_idx=env_idx,
-                    vbd_trajectory=self.vbd_trajectory,
+                    trajectory=self.trajectory,
                     line_width_scale=line_width_scale,
                 )
 
@@ -389,9 +378,12 @@ def plot_simulator_state(
 
             ax.set_axis_off()
 
-        for fig in figs:
+            # Apply tight layout to current figure
             fig.tight_layout(pad=2, rect=[0.00, 0.00, 0.9, 1])
 
+            # Close the figure to prevent memory leaks and cleanup
+            plt.close(fig)
+
         return figs
 
     def plot_agent_trajectories(
@@ -626,173 +618,125 @@ def plot_agent_trajectories(
 
         return None
 
-    def _plot_waypoints(
+    def _plot_reference_xy(
         self,
         ax: matplotlib.axes.Axes,
         env_idx: int,
         control_mask: torch.Tensor,
-        log_trajectory: LogTrajectory,
+        trajectory,
         line_width_scale: int = 1.0,
     ):
         """Plot the log replay trajectory for controlled agents in either 2D or 3D."""
         if self.render_3d:
-            # Get trajectory points
-            trajectory_points = log_trajectory.pos_xy[
-                env_idx, control_mask, :, :
-            ].numpy()
-
-            # Set a fixed height for trajectory visualization
-            trajectory_height = 0.05  # Small height above ground
-
-            # Plot trajectories for each controlled agent
-            for agent_trajectory in trajectory_points:
-                # Filter out invalid points (zeros or out of bounds)
-                valid_mask = (
-                    (agent_trajectory[:, 0] != 0)
-                    & (agent_trajectory[:, 1] != 0)
-                    & (np.abs(agent_trajectory[:, 0]) < OUT_OF_BOUNDS)
-                    & (np.abs(agent_trajectory[:, 1]) < OUT_OF_BOUNDS)
+            # Get trajectory points - make a clean copy to avoid reference issues
+            try:
+                trajectory_points = (
+                    trajectory.pos_xy[env_idx, control_mask, :, :]
+                    .clone()
+                    .numpy()
                 )
-                valid_points = agent_trajectory[valid_mask]
 
-                if len(valid_points) > 1:
-                    # Create segments for the trajectory
-                    segments = []
-                    for i in range(len(valid_points) - 1):
-                        segment = np.array(
-                            [
-                                [
-                                    valid_points[i, 0],
-                                    valid_points[i, 1],
-                                    trajectory_height,
-                                ],
+                # Set a fixed height for trajectory visualization
+                trajectory_height = 0.05  # Small height above ground
+
+                # Plot trajectories for each controlled agent
+                for agent_trajectory in trajectory_points:
+                    # Filter out invalid points (zeros or out of bounds)
+                    valid_mask = (
+                        (agent_trajectory[:, 0] != 0)
+                        & (agent_trajectory[:, 1] != 0)
+                        & (np.abs(agent_trajectory[:, 0]) < OUT_OF_BOUNDS)
+                        & (np.abs(agent_trajectory[:, 1]) < OUT_OF_BOUNDS)
+                    )
+                    valid_points = agent_trajectory[valid_mask]
+
+                    if len(valid_points) > 1:
+                        # Create segments for the trajectory
+                        segments = []
+                        for i in range(len(valid_points) - 1):
+                            segment = np.array(
                                 [
-                                    valid_points[i + 1, 0],
-                                    valid_points[i + 1, 1],
-                                    trajectory_height,
-                                ],
-                            ]
-                        )
-                        segments.append(segment)
+                                    [
+                                        valid_points[i, 0],
+                                        valid_points[i, 1],
+                                        trajectory_height,
+                                    ],
+                                    [
+                                        valid_points[i + 1, 0],
+                                        valid_points[i + 1, 1],
+                                        trajectory_height,
+                                    ],
+                                ]
+                            )
+                            segments.append(segment)
 
-                    # Create line collection with fade effect
-                    colors = np.zeros((len(segments), 4))
-                    colors[:, 1] = 0.9  # Green component
-                    colors[:, 3] = np.linspace(
-                        0.2, 0.6, len(segments)
-                    )  # Alpha gradient
+                        # Create line collection with fade effect
+                        colors = np.zeros((len(segments), 4))
+                        colors[:, 1] = 0.9  # Green component
+                        colors[:, 3] = np.linspace(
+                            0.2, 0.6, len(segments)
+                        )  # Alpha gradient
 
-                    lc = Line3DCollection(
-                        segments, colors=colors, linewidth=2 * line_width_scale
-                    )
-                    ax.add_collection3d(lc)
+                        # Create a fresh line collection for each plot
+                        lc = Line3DCollection(
+                            segments,
+                            colors=colors,
+                            linewidth=2 * line_width_scale,
+                        )
+                        ax.add_collection3d(lc)
 
-                    # Add points at trajectory positions
-                    ax.scatter3D(
-                        valid_points[:, 0],
-                        valid_points[:, 1],
-                        np.full_like(valid_points[:, 0], trajectory_height),
-                        color="lightgreen",
-                        s=10,
-                        alpha=0.5,
-                        zorder=0,
-                    )
+                        # Add points at trajectory positions - creating a new scatter object each time
+                        ax.scatter3D(
+                            valid_points[:, 0],
+                            valid_points[:, 1],
+                            np.full_like(
+                                valid_points[:, 0], trajectory_height
+                            ),
+                            color="lightgreen",
+                            s=10,
+                            alpha=0.5,
+                            zorder=0,
+                        )
+            except Exception as e:
+                print(f"Error plotting 3D reference trajectory: {e}")
         else:
-            # Original 2D plotting
-            ax.scatter(
-                log_trajectory.pos_xy[env_idx, control_mask, :, 0]
-                .cpu()
-                .numpy(),
-                log_trajectory.pos_xy[env_idx, control_mask, :, 1]
-                .cpu()
-                .numpy(),
-                color="lightgreen",
-                linewidth=0.35 * line_width_scale,
-                alpha=0.35,
-                zorder=0,
-            )
-
-    def _plot_vbd_trajectory(
-        self,
-        ax: matplotlib.axes.Axes,
-        env_idx: int,
-        control_mask: torch.Tensor,
-        vbd_trajectory: VBDTrajectory,
-        line_width_scale: int = 1.0,
-    ):
-        """Plot the VBD trajectory for controlled agents in either 2D or 3D."""
-        if self.render_3d:
-            # Get trajectory points
-            trajectory_points = vbd_trajectory.pos_xy[
-                env_idx, control_mask, :, :
-            ].numpy()
-
-            # Set a fixed height for trajectory visualization
-            trajectory_height = 0.05
+            try:
+                # Create a new scatter plot for this specific environment and control mask
+                pos_x = (
+                    trajectory.pos_xy.clone()[env_idx, control_mask, :, 0]
+                    .cpu()
+                    .numpy()
+                )
+                pos_y = (
+                    trajectory.pos_xy.clone()[env_idx, control_mask, :, 1]
+                    .cpu()
+                    .numpy()
+                )
 
-            # Plot trajectories for each controlled agent
-            for agent_trajectory in trajectory_points:
                 # Filter out invalid points (zeros or out of bounds)
                 valid_mask = (
-                    (agent_trajectory[:, 0] != 0)
-                    & (agent_trajectory[:, 1] != 0)
-                    & (np.abs(agent_trajectory[:, 0]) < OUT_OF_BOUNDS)
-                    & (np.abs(agent_trajectory[:, 1]) < OUT_OF_BOUNDS)
+                    (pos_x != 0)
+                    & (pos_y != 0)
+                    & (np.abs(pos_x) < OUT_OF_BOUNDS)
+                    & (np.abs(pos_y) < OUT_OF_BOUNDS)
                 )
-                valid_points = agent_trajectory[valid_mask]
 
-                if len(valid_points) > 1:
-                    # Create segments for the trajectory
-                    segments = []
-                    for i in range(len(valid_points) - 1):
-                        segment = np.array(
-                            [
-                                [
-                                    valid_points[i, 0],
-                                    valid_points[i, 1],
-                                    trajectory_height,
-                                ],
-                                [
-                                    valid_points[i + 1, 0],
-                                    valid_points[i + 1, 1],
-                                    trajectory_height,
-                                ],
-                            ]
-                        )
-                        segments.append(segment)
-
-                    # Create line collection with fade effect
-                    colors = np.zeros((len(segments), 4))
-                    colors[:, 1] = 0.9  # Green component
-                    colors[:, 3] = np.linspace(
-                        0.2, 0.6, len(segments)
-                    )  # Alpha gradient
-
-                    lc = Line3DCollection(
-                        segments, colors=colors, linewidth=2 * line_width_scale
-                    )
-                    ax.add_collection3d(lc)
+                # Apply mask if any valid points exist
+                if np.any(valid_mask):
+                    pos_x = pos_x[valid_mask]
+                    pos_y = pos_y[valid_mask]
 
-                    # Add points at trajectory positions
-                    ax.scatter3D(
-                        valid_points[:, 0],
-                        valid_points[:, 1],
-                        np.full_like(valid_points[:, 0], trajectory_height),
+                    # Create a fresh scatter plot
+                    ax.scatter(
+                        pos_x,
+                        pos_y,
                         color="lightgreen",
-                        s=10,
-                        alpha=0.5,
+                        linewidth=0.3 * line_width_scale,
+                        alpha=0.25,
                         zorder=0,
                     )
-        else:
-            # Original 2D plotting
-            ax.scatter(
-                vbd_trajectory.pos_xy[env_idx, control_mask, :, 0].numpy(),
-                vbd_trajectory.pos_xy[env_idx, control_mask, :, 1].numpy(),
-                color="lightgreen",
-                linewidth=0.35 * line_width_scale,
-                alpha=0.35,
-                zorder=0,
-            )
+            except Exception as e:
+                print(f"Error plotting 2D reference trajectory: {e}")
 
     def _plot_vbd_trajectory(
         self,

From a7512ab511bee6ea7dc745dd6098f7f579f99308 Mon Sep 17 00:00:00 2001
From: Daphne Cornelisse <33460159+daphne-cornelisse@users.noreply.github.com>
Date: Tue, 29 Apr 2025 16:15:40 -0400
Subject: [PATCH 02/38] Add WOSAC initialization modes (#426)

* detect parked script

* default arg change

* parked vehicle mask

* train and eval init modes

* Minor fixes

---------

Co-authored-by: kevin <kevinwinston184@gmail.com>
---
 baselines/ppo/config/ppo_guided_autonomy.yaml |   2 +-
 data_utils/detect_parked.py                   | 338 ++++++++++++++++++
 examples/eval/run_wosac_eval.py               |  21 +-
 gpudrive/datatypes/info.py                    |   2 +
 gpudrive/env/base_env.py                      |   7 +-
 gpudrive/env/config.py                        |   2 +-
 gpudrive/env/env_torch.py                     |   5 +-
 gpudrive/integrations/vbd/data/amortize.py    |   2 +-
 src/bindings.cpp                              |   3 +-
 src/init.hpp                                  |   3 +-
 src/level_gen.cpp                             |   8 +
 11 files changed, 382 insertions(+), 11 deletions(-)
 create mode 100644 data_utils/detect_parked.py

diff --git a/baselines/ppo/config/ppo_guided_autonomy.yaml b/baselines/ppo/config/ppo_guided_autonomy.yaml
index e3395d25d..ec8da96eb 100644
--- a/baselines/ppo/config/ppo_guided_autonomy.yaml
+++ b/baselines/ppo/config/ppo_guided_autonomy.yaml
@@ -34,7 +34,7 @@ environment: # Overrides default environment configs (see pygpudrive/env/config.
   guidance_heading_weight: 0.01
   smoothness_weight: 0.001
 
-  init_mode: womd_tracks_to_predict
+  init_mode: wosac_train
   dynamics_model: "classic"
   remove_non_vehicles: false
   collision_behavior: "ignore"
diff --git a/data_utils/detect_parked.py b/data_utils/detect_parked.py
new file mode 100644
index 000000000..7a3750e7e
--- /dev/null
+++ b/data_utils/detect_parked.py
@@ -0,0 +1,338 @@
+import os
+import json
+import logging
+import psutil
+import argparse
+import math
+from pathlib import Path
+import numpy as np
+from multiprocessing import Pool, cpu_count
+from tqdm import tqdm
+import trimesh
+
+logging.basicConfig(level=logging.INFO)
+
+def _filter_small_segments(segments, min_length=1e-6):
+    """Filter out segments that are too short."""
+    valid_segments = []
+    for segment in segments:
+        start, end = segment
+        length = np.linalg.norm(np.array(end) - np.array(start))
+        if length >= min_length:
+            valid_segments.append(segment)
+    return valid_segments
+
+
+def _generate_mesh(segments, height=2.0, width=0.2):
+    """Generate a mesh from line segments."""
+    segments = np.array(segments, dtype=np.float64)
+    starts, ends = segments[:, 0, :], segments[:, 1, :]
+    directions = ends - starts
+    lengths = np.linalg.norm(directions, axis=1, keepdims=True)
+    unit_directions = directions / lengths
+
+    # Create the base box mesh with the height along the z-axis
+    base_box = trimesh.creation.box(extents=[1.0, width, height])
+    base_box.apply_translation([0.5, 0, 0])  # Align box's origin to its start
+    z_axis = np.array([0, 0, 1])
+    angles = np.arctan2(
+        unit_directions[:, 1], unit_directions[:, 0]
+    )  # Rotation in the XY plane
+
+    rectangles = []
+    lengths = lengths.flatten()
+
+    for i, (start, length, angle) in enumerate(zip(starts, lengths, angles)):
+        # Copy the base box and scale to match segment length
+        scaled_box = base_box.copy()
+        scaled_box.apply_scale([length, 1.0, 1.0])
+
+        # Apply rotation around the z-axis
+        rotation_matrix = trimesh.transformations.rotation_matrix(
+            angle, z_axis
+        )
+        scaled_box.apply_transform(rotation_matrix)
+
+        # Translate the box to the segment's starting point
+        scaled_box.apply_translation(start)
+
+        rectangles.append(scaled_box)
+
+    # Concatenate all boxes into a single mesh
+    mesh = trimesh.util.concatenate(rectangles)
+    return mesh
+
+
+def _create_agent_box_mesh(position, heading, length, width, height):
+    """Create a box mesh for an agent at a given position and orientation.
+    
+    Args:
+        position (list): [x, y, z] position
+        heading (float): yaw angle in radians
+        length (float): length of the box
+        width (float): width of the box 
+        height (float): height of the box
+        
+    Returns:
+        trimesh.Trimesh: Box mesh positioned and oriented correctly
+    """
+    # Create box centered at origin
+    box = trimesh.creation.box(extents=[length, width, height])
+    
+    # Rotate box to align with heading
+    z_axis = np.array([0, 0, 1])
+    rotation_matrix = trimesh.transformations.rotation_matrix(heading, z_axis)
+    box.apply_transform(rotation_matrix)
+    
+    # Move box to position
+    box.apply_translation(position)
+    
+    return box
+
+
+def calculate_trajectory_length(positions, valid_mask, start_idx, end_idx=90):
+    """
+    Calculate the sum of valid trajectory segments from start_idx to end_idx.
+    
+    Args:
+        positions: List of position dictionaries with x, y, z coordinates
+        valid_mask: List of boolean values indicating valid timesteps
+        start_idx: Starting index for calculation
+        end_idx: Ending index for calculation
+        
+    Returns:
+        float: Sum of valid trajectory segments length
+    """
+    # Limit end_idx to the maximum available index
+    end_idx = min(end_idx, len(positions) - 1)
+    
+    # Initialize total length
+    total_length = 0.0
+    
+    # Iterate through positions from start_idx to end_idx
+    for i in range(start_idx, end_idx):
+        # Check if both current and next positions are valid
+        if valid_mask[i] and valid_mask[i + 1]:
+            # Calculate distance between consecutive valid positions
+            current_pos = np.array([positions[i]['x'], positions[i]['y'], positions[i]['z']])
+            next_pos = np.array([positions[i + 1]['x'], positions[i + 1]['y'], positions[i + 1]['z']])
+                
+            segment_length = np.linalg.norm(next_pos - current_pos)
+            total_length += segment_length
+            
+    return total_length
+
+
+def process_scene(args):
+    """Process a single scene file to detect parked vehicles."""
+    filepath, init_steps, threshold = args
+    try:
+        with open(filepath, 'r') as f:
+            scene = json.load(f)
+
+        # Initialize counters for this scene
+        valid_vehicles = 0
+        colliding_vehicles = 0
+        parked_vehicles = 0
+        moving_vehicles = 0
+
+        # Extract road data for collision checking
+        roads = scene['roads']
+        edge_segments = []
+
+        # Collect road edge segments for collision checking
+        for road in roads:
+            if road["type"] == "road_edge":
+                edge_vertices = [[r["x"], r["y"], r["z"]] for r in road["geometry"]]
+                edge_segments.extend([
+                    [edge_vertices[i], edge_vertices[i + 1]]
+                    for i in range(len(edge_vertices) - 1)
+                ])
+
+        # Generate road edge mesh
+        edge_segments = _filter_small_segments(edge_segments)
+        edge_mesh = _generate_mesh(edge_segments)
+
+        # Create collision managers
+        road_collision_manager = trimesh.collision.CollisionManager()
+        road_collision_manager.add_object("road_edges", edge_mesh)
+        vehicle_collision_manager = trimesh.collision.CollisionManager()
+
+        # First, collect all valid vehicles at init_steps
+        valid_vehicle_objects = []
+        for obj in scene['objects']:
+            # Check if object is a vehicle and valid at init_steps
+            if (obj['type'] == 'vehicle' and 
+                init_steps < len(obj['valid']) and 
+                obj['valid'][init_steps]):
+                
+                valid_vehicles += 1
+                valid_vehicle_objects.append(obj)
+                
+                # Create vehicle box at init_steps
+                position = [
+                    obj['position'][init_steps]['x'],
+                    obj['position'][init_steps]['y'],
+                    obj['position'][init_steps]['z']
+                ]
+                heading = obj['heading'][init_steps]
+                vehicle_box = _create_agent_box_mesh(
+                    position,
+                    heading,
+                    obj['length'],
+                    obj['width'],
+                    obj['height']
+                )
+                vehicle_collision_manager.add_object(str(obj['id']), vehicle_box)
+
+        # Check for collisions with other vehicles
+        _, vehicle_collision_pairs = vehicle_collision_manager.in_collision_internal(return_names=True)
+        
+        # Check for collisions with road edges
+        _, road_collision_pairs = vehicle_collision_manager.in_collision_other(
+            road_collision_manager, return_names=True
+        )
+        
+        # Combine all colliding vehicle IDs
+        colliding_vehicle_ids = set()
+        
+        # Add vehicles that collide with each other
+        for v1, v2 in vehicle_collision_pairs:
+            colliding_vehicle_ids.add(v1)
+            colliding_vehicle_ids.add(v2)
+        
+        # Add vehicles that collide with road edges
+        for v_id, _ in road_collision_pairs:
+            colliding_vehicle_ids.add(v_id)
+            
+        colliding_vehicles = len(colliding_vehicle_ids)
+        
+        # Process each colliding vehicle to determine if parked or moving
+        for obj in valid_vehicle_objects:
+            if str(obj['id']) in colliding_vehicle_ids:
+                # Calculate trajectory length from init_steps to 90 or last timestep
+                trajectory_length = calculate_trajectory_length(
+                    obj['position'], 
+                    obj['valid'], 
+                    init_steps,
+                    min(90, len(obj['valid']) - 1)
+                )
+                
+                # Classify as parked or moving based on threshold
+                if trajectory_length < threshold:
+                    parked_vehicles += 1
+                else:
+                    moving_vehicles += 1
+
+        return filepath, (valid_vehicles, colliding_vehicles, parked_vehicles, moving_vehicles)
+
+    except Exception as e:
+        logging.error(f"Error processing {filepath}: {e}")
+        return filepath, None
+
+
+def process_directory(args):
+    """Process all JSON files in directory."""
+    input_dir = Path(args.input_dir)
+    num_workers = args.num_workers
+    init_steps = args.init_steps
+    threshold = args.threshold
+
+    # Get all JSON files
+    json_files = list(input_dir.glob("*.json"))
+    if not json_files:
+        logging.error(f"No JSON files found in {input_dir}")
+        return
+
+    logging.info(f"Found {len(json_files)} JSON files to process")
+
+    # Calculate batch size based on available memory
+    mem_info = psutil.virtual_memory()
+    available_memory = mem_info.available / (1024**3)  # Convert to GB
+    usable_memory = int(available_memory * 0.9)  # Use 90% of available memory
+    batch_size = min(1000 * usable_memory, len(json_files))
+    
+    # Initialize counters using numpy int64 to handle large numbers
+    total_processed = np.int64(0)
+    total_valid_vehicles = np.int64(0)
+    total_colliding_vehicles = np.int64(0)
+    total_parked_vehicles = np.int64(0)
+    total_moving_vehicles = np.int64(0)
+    
+    # Process files in batches
+    for i in range(0, len(json_files), int(batch_size)):
+        batch = json_files[i:i + int(batch_size)]
+        
+        # Process batch in parallel
+        with Pool(num_workers) as pool:
+            results = list(tqdm(
+                pool.imap(process_scene, [(str(f), init_steps, threshold) for f in batch]),
+                total=len(batch),
+                desc=f"Processing batch {i//int(batch_size) + 1}"
+            ))
+        
+        # Count results
+        for filepath, counts in results:
+            if counts is not None:
+                valid_vehicles, colliding_vehicles, parked_vehicles, moving_vehicles = counts
+                total_processed += 1
+                total_valid_vehicles += valid_vehicles
+                total_colliding_vehicles += colliding_vehicles
+                total_parked_vehicles += parked_vehicles
+                total_moving_vehicles += moving_vehicles
+    
+    # Calculate percentages using float64 for precision
+    parked_percentage = (float(total_parked_vehicles) / float(total_colliding_vehicles) * 100) if total_colliding_vehicles > 0 else 0.0
+    moving_percentage = (float(total_moving_vehicles) / float(total_colliding_vehicles) * 100) if total_colliding_vehicles > 0 else 0.0
+    
+    logging.info(f"Processing complete!")
+    logging.info(f"Total files processed: {total_processed:,d}")
+    logging.info(f"Total vehicles valid at t={init_steps}: {total_valid_vehicles:,d}")
+    logging.info(f"Total vehicles in collision at t={init_steps}: {total_colliding_vehicles:,d}")
+    logging.info(f"Total parked vehicles: {total_parked_vehicles:,d} ({parked_percentage:.2f}%)")
+    logging.info(f"Total moving vehicles: {total_moving_vehicles:,d} ({moving_percentage:.2f}%)")
+    
+    # Save results to a JSON file for future reference
+    results = {
+        "total_files_processed": int(total_processed),
+        "total_valid_vehicles": int(total_valid_vehicles),
+        "total_colliding_vehicles": int(total_colliding_vehicles),
+        "total_parked_vehicles": int(total_parked_vehicles),
+        "total_moving_vehicles": int(total_moving_vehicles),
+        "parked_percentage": float(parked_percentage),
+        "moving_percentage": float(moving_percentage)
+    }
+    
+    with open('parked_vehicle_results.json', 'w') as f:
+        json.dump(results, f, indent=4)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Analyze vehicle parking behavior in JSON files"
+    )
+    parser.add_argument(
+        "input_dir",
+        help="Directory containing JSON files to process"
+    )
+    parser.add_argument(
+        "--num_workers",
+        type=int,
+        default=cpu_count(),
+        help="Number of worker processes (default: number of CPU cores)"
+    )
+    parser.add_argument(
+        "--init_steps",
+        type=int,
+        default=10,
+        help="Initial timestep to check for vehicle validity and collisions (default: 10)"
+    )
+    parser.add_argument(
+        "--threshold",
+        type=float,
+        default=1.0,
+        help="Threshold for trajectory length to classify a vehicle as moving (default: 0.2)"
+    )
+
+    args = parser.parse_args()
+    process_directory(args)
\ No newline at end of file
diff --git a/examples/eval/run_wosac_eval.py b/examples/eval/run_wosac_eval.py
index a939cc8aa..6e5178737 100644
--- a/examples/eval/run_wosac_eval.py
+++ b/examples/eval/run_wosac_eval.py
@@ -13,8 +13,10 @@
 from gpudrive.env.env_torch import GPUDriveTorchEnv
 from gpudrive.env.dataset import SceneDataLoader
 from gpudrive.datatypes.observation import GlobalEgoState
+from gpudrive.datatypes.info import Info
 from gpudrive.utils.checkpoint import load_agent
 from gpudrive.visualize.utils import img_from_fig
+import madrona_gpudrive
 
 # WOSAC
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
@@ -67,7 +69,19 @@ def rollout(
 
     start_env_rollout = perf_counter()
 
-    control_mask = env.cont_agent_mask
+    _ = env.reset()
+
+    # Zero out actions for parked vehicles
+    info = Info.from_tensor(
+        env.sim.info_tensor(),
+        backend=env.backend,
+        device=env.device,
+    )
+    control_mask_all = env.cont_agent_mask.clone()
+    zero_action_mask = (info.off_road == 1) | (
+        info.collided_with_vehicle == 1
+    ) & (info.type == int(madrona_gpudrive.EntityType.Vehicle))
+    control_mask = control_mask_all & ~zero_action_mask
 
     next_obs = env.reset(control_mask)
 
@@ -217,7 +231,7 @@ def rollout(
     NUM_ENVS = 3
     DEVICE = "cuda"  # where to run the env rollouts
     NUM_ROLLOUTS_PER_BATCH = 1
-    NUM_DATA_BATCHES = 2
+    NUM_DATA_BATCHES = 1
     INIT_STEPS = 10
     DATASET_SIZE = 100
     RENDER = True
@@ -238,7 +252,8 @@ def rollout(
     # Load agent
     agent = load_agent(
         # path_to_cpt="checkpoints/model_guidance_log_replay__S_1__04_26_09_02_20_677_000833.pt",
-        path_to_cpt="checkpoints/model_guidance_log_replay__S_3__04_27_13_13_33_780_013762.pt",
+        # path_to_cpt="checkpoints/model_guidance_log_replay__S_3__04_27_13_13_33_780_013762.pt", # Trained with collision penalty
+        path_to_cpt="checkpoints/model_guidance_log_replay__S_3__04_28_15_56_44_152_014083.pt",  # Trained without collision penalty
     ).to(DEVICE)
 
     # Override default environment settings to match those the agent was trained with
diff --git a/gpudrive/datatypes/info.py b/gpudrive/datatypes/info.py
index 3556294df..c0cca1a84 100644
--- a/gpudrive/datatypes/info.py
+++ b/gpudrive/datatypes/info.py
@@ -12,7 +12,9 @@ def __init__(self, info_tensor: torch.Tensor):
         """Initializes the ego state with an observation tensor."""
         self.off_road = info_tensor[:, :, 0]
         self.collided = info_tensor[:, :, 1:3].sum(axis=2)
+        self.collided_with_vehicle = info_tensor[:, :, 1]
         self.goal_achieved = info_tensor[:, :, 3]
+        self.type = info_tensor[:, :, 4]
 
     @classmethod
     def from_tensor(
diff --git a/gpudrive/env/base_env.py b/gpudrive/env/base_env.py
index 7c5afc16b..cb1258238 100755
--- a/gpudrive/env/base_env.py
+++ b/gpudrive/env/base_env.py
@@ -117,11 +117,16 @@ def _setup_environment_parameters(self):
         )
         params.rewardParams = self._set_reward_params()
         params.maxNumControlledAgents = self.max_cont_agents
-        if self.config.init_mode == "womd_tracks_to_predict":
+        if self.config.init_mode == "wosac_eval":
             # Bypasses all gpudrive initialization rules and directly reads from the tracks_to_predict
             # flag in the WOMD dataset metadata
             params.readFromTracksToPredict = True
             params.isStaticAgentControlled = True
+            params.controlExperts = True
+        elif self.config.init_mode == "wosac_train":
+            params.readFromTracksToPredict = True
+            params.isStaticAgentControlled = True
+            params.controlExperts = False
         elif self.config.init_mode == "all_objects":
             params.isStaticAgentControlled = True
             params.initOnlyValidAgentsAtFirstStep = False
diff --git a/gpudrive/env/config.py b/gpudrive/env/config.py
index b1fb1ddf0..9e3af7609 100755
--- a/gpudrive/env/config.py
+++ b/gpudrive/env/config.py
@@ -173,7 +173,7 @@ class EnvConfig:
     agent_size_scale: float = madrona_gpudrive.vehicleScale
 
     # Initialization mode
-    init_mode: str = "all_non_trivial"  # Options: all_non_trivial, all_objects, all_valid, womd_tracks_to_predict
+    init_mode: str = "all_non_trivial"  # Options: all_non_trivial, all_objects, all_valid, wosac_eval, wosac_train
 
     # VBD model settings
     use_vbd: bool = False
diff --git a/gpudrive/env/env_torch.py b/gpudrive/env/env_torch.py
index 60cec0fef..cdafc62ca 100755
--- a/gpudrive/env/env_torch.py
+++ b/gpudrive/env/env_torch.py
@@ -1704,12 +1704,13 @@ def get_scenario_ids(self):
         add_reference_speed=True,
         add_reference_heading=True,
         reward_type="guided_autonomy",
+        init_mode="wosac_train",
     )
     render_config = RenderConfig()
 
     # Create data loader
     train_loader = SceneDataLoader(
-        root="data/processed/examples",
+        root="data/processed/wosac/debug",
         batch_size=1,
         dataset_size=1,
         sample_with_replacement=False,
@@ -1753,7 +1754,7 @@ def get_scenario_ids(self):
     print(f"Highlighted agent: {highlight_agent}")
     print(f"Position: {agent_positions[-1]}")
 
-    for t in range(env.init_steps, env.episode_len):
+    for t in range(env.init_steps, env.init_steps + 10):
         print(f"Step: {t+1}")
 
         # Step the environment
diff --git a/gpudrive/integrations/vbd/data/amortize.py b/gpudrive/integrations/vbd/data/amortize.py
index 194926203..35fa375d6 100644
--- a/gpudrive/integrations/vbd/data/amortize.py
+++ b/gpudrive/integrations/vbd/data/amortize.py
@@ -64,7 +64,7 @@ def main():
         init_steps=INIT_STEPS, # Warmup period
         dynamics_model="state", # Use state-based dynamics model
         dist_to_goal_threshold=1e-5, # Trick to make sure the agents don't disappear when they reach the goal
-        init_mode = 'womd_tracks_to_predict',
+        init_mode = 'wosac_eval',
         max_controlled_agents=MAX_CONTROLLED_AGENTS,
         goal_behavior='ignore'
     )
diff --git a/src/bindings.cpp b/src/bindings.cpp
index e44ff4619..e3edfe6cd 100755
--- a/src/bindings.cpp
+++ b/src/bindings.cpp
@@ -61,7 +61,8 @@ namespace madrona_gpudrive
             .def_rw("disableClassicalObs", &Parameters::disableClassicalObs)
             .def_rw("isStaticAgentControlled", &Parameters::isStaticAgentControlled)
             .def_rw("readFromTracksToPredict", &Parameters::readFromTracksToPredict)
-            .def_rw("initSteps", &Parameters::initSteps);
+            .def_rw("initSteps", &Parameters::initSteps)
+            .def_rw("controlExperts", &Parameters::controlExperts);
 
         // Define CollisionBehaviour enum
         nb::enum_<CollisionBehaviour>(m, "CollisionBehaviour")
diff --git a/src/init.hpp b/src/init.hpp
index 0c7e25d3c..8d4ff60dc 100755
--- a/src/init.hpp
+++ b/src/init.hpp
@@ -131,8 +131,9 @@ namespace madrona_gpudrive
         bool enableLidar = false;
         bool disableClassicalObs = false;
         DynamicsModel dynamicsModel = DynamicsModel::Classic;
-        bool readFromTracksToPredict = false;       // Default: false - for womd_tracks_to_predict initialization mode
+        bool readFromTracksToPredict = false;       // Default: false - for wosac initialization mode
         uint32_t initSteps = 0;
+        bool controlExperts = false; // Default: false - for wosac initialization mode
     };
 
     struct WorldInit
diff --git a/src/level_gen.cpp b/src/level_gen.cpp
index 76da34ed7..c43ad403f 100755
--- a/src/level_gen.cpp
+++ b/src/level_gen.cpp
@@ -133,12 +133,20 @@ static inline bool isAgentControllable(Engine &ctx, Entity agent, bool markAsExp
         if (ctx.get<Trajectory>(agent_iface).valids[initSteps] == 0) {
             return false;
         }
+        else if (!ctx.data().params.controlExperts && markAsExpert) {
+            return false;
+        }
         else {
             return ctx.data().numControlledAgents < ctx.data().params.maxNumControlledAgents;
         }
     }
     
     // Original logic for other initialization modes
+    if (ctx.data().params.controlExperts) {
+        return ctx.data().numControlledAgents < ctx.data().params.maxNumControlledAgents &&
+           ctx.get<Trajectory>(agent_iface).valids[0] &&
+           ctx.get<ResponseType>(agent) == ResponseType::Dynamic;        
+    }
     return ctx.data().numControlledAgents < ctx.data().params.maxNumControlledAgents &&
            ctx.get<Trajectory>(agent_iface).valids[0] &&
            ctx.get<ResponseType>(agent) == ResponseType::Dynamic &&

From 131626be3d0d2c2129d505b3eae3e57ff8e70a05 Mon Sep 17 00:00:00 2001
From: Daphne Cornelisse <33460159+daphne-cornelisse@users.noreply.github.com>
Date: Tue, 29 Apr 2025 19:28:09 -0400
Subject: [PATCH 03/38] Better agent POV visualization and add LiDAr (#424)

* Improve agent pov plotting utils

* Delete old unused function

* Fix

* Add lidar obs option to plot agent observation

* wip

* Revert defaults
---
 baselines/ppo/config/ppo_guided_autonomy.yaml |     6 +-
 examples/eval/notebooks/wosac_results.ipynb   |   319 +-
 examples/experimental/notebooks/debug.ipynb   | 32653 +++++++++++++++-
 gpudrive/datatypes/observation.py             |     9 +-
 gpudrive/env/env_torch.py                     |     8 +-
 gpudrive/visualize/color.py                   |     1 +
 gpudrive/visualize/core.py                    |   179 +-
 gpudrive/visualize/utils.py                   |    24 +-
 8 files changed, 32732 insertions(+), 467 deletions(-)

diff --git a/baselines/ppo/config/ppo_guided_autonomy.yaml b/baselines/ppo/config/ppo_guided_autonomy.yaml
index ec8da96eb..458058111 100644
--- a/baselines/ppo/config/ppo_guided_autonomy.yaml
+++ b/baselines/ppo/config/ppo_guided_autonomy.yaml
@@ -27,8 +27,8 @@ environment: # Overrides default environment configs (see pygpudrive/env/config.
 
   # Reward function
   reward_type: "guided_autonomy"
-  collision_weight: -0.2
-  off_road_weight: -0.2
+  collision_weight: -0.01
+  off_road_weight: -0.01
   guidance_pos_xy_weight: 0.01
   guidance_speed_weight: 0.01
   guidance_heading_weight: 0.01
@@ -48,8 +48,6 @@ environment: # Overrides default environment configs (see pygpudrive/env/config.
   max_accel_value: 4.0
   init_steps: 0 # Warmup steps
   goal_achieved_weight: 0.0
-  collision_weight: -0.2
-  off_road_weight: -0.2
 
 wandb:
   entity: ""
diff --git a/examples/eval/notebooks/wosac_results.ipynb b/examples/eval/notebooks/wosac_results.ipynb
index e76e80422..c63b2ad1c 100644
--- a/examples/eval/notebooks/wosac_results.ipynb
+++ b/examples/eval/notebooks/wosac_results.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,25 +31,45 @@
     "    df = pd.read_csv(most_recent_file)\n",
     "    df = df.set_index(\"AGENT POLICY\")\n",
     "    \n",
-    "    def highlight_best(df):\n",
-    "        styles = pd.DataFrame('', index=df.index, columns=df.columns)\n",
-    "        \n",
+    "    def heatmap_best_bold(s):\n",
     "        higher_better_cols = [col for col in df.columns if '(↑)' in col]\n",
     "        lower_better_cols = [col for col in df.columns if '(↓)' in col]\n",
     "        \n",
+    "        styles = pd.DataFrame('', index=df.index, columns=df.columns)\n",
+    "        \n",
+    "        # For higher-better columns (green)\n",
     "        for col in higher_better_cols:\n",
     "            max_val = df[col].max()\n",
-    "            max_indices = df[col] == max_val\n",
-    "            styles.loc[max_indices, col] = 'background-color: #a8f0a8'\n",
+    "            min_val = df[col].min()\n",
+    "            range_val = max_val - min_val\n",
+    "            \n",
+    "            for idx in df.index:\n",
+    "                val = df.loc[idx, col]\n",
+    "                if range_val > 0:\n",
+    "                    norm_val = (val - min_val) / range_val\n",
+    "                    styles.loc[idx, col] = f'background-color: rgba(0, 255, 0, {norm_val * 0.5})'\n",
+    "                if val == max_val:\n",
+    "                    styles.loc[idx, col] += '; font-weight: bold'\n",
     "        \n",
+    "        # For lower-better columns (pastel red)\n",
     "        for col in lower_better_cols:\n",
     "            min_val = df[col].min()\n",
-    "            min_indices = df[col] == min_val\n",
-    "            styles.loc[min_indices, col] = 'background-color: #a8f0a8'\n",
+    "            max_val = df[col].max()\n",
+    "            range_val = max_val - min_val\n",
+    "            \n",
+    "            for idx in df.index:\n",
+    "                val = df.loc[idx, col]\n",
+    "                if range_val > 0:\n",
+    "                    norm_val = 1 - ((val - min_val) / range_val)\n",
+    "                    # Using pastel red (255, 180, 180)\n",
+    "                    styles.loc[idx, col] = f'background-color: rgba(255, 180, 180, {norm_val * 0.5})'\n",
+    "                if val == min_val:\n",
+    "                    styles.loc[idx, col] += '; font-weight: bold'\n",
     "        \n",
     "        return styles\n",
     "    \n",
-    "    styled_df = df.style.apply(highlight_best, axis=None).format(precision=3)\n",
+    "    # Format the numbers without modifying the dataframe\n",
+    "    styled_df = df.style.apply(heatmap_best_bold, axis=None).format(precision=3)\n",
     "    \n",
     "    return styled_df\n",
     "\n",
@@ -113,43 +133,138 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Using file: ../figures_data/wosac_table_2025-04-24 09.csv\n"
+      "Using file: ../figures_data/wosac_table_2025-04-29 12.csv\n"
      ]
     },
     {
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_1b1dc_row0_col2, #T_1b1dc_row0_col4, #T_1b1dc_row0_col5, #T_1b1dc_row0_col6, #T_1b1dc_row0_col8, #T_1b1dc_row0_col9, #T_1b1dc_row0_col10, #T_1b1dc_row0_col11, #T_1b1dc_row0_col12, #T_1b1dc_row1_col1, #T_1b1dc_row1_col3, #T_1b1dc_row1_col7, #T_1b1dc_row1_col13, #T_1b1dc_row1_col14 {\n",
-       "  background-color: #a8f0a8;\n",
+       "#T_489db_row0_col1 {\n",
+       "  background-color: rgba(0, 255, 0, 0.3145877437142511);\n",
+       "}\n",
+       "#T_489db_row0_col2 {\n",
+       "  background-color: rgba(0, 255, 0, 0.4740065635205114);\n",
+       "}\n",
+       "#T_489db_row0_col3 {\n",
+       "  background-color: rgba(0, 255, 0, 0.4906903910771463);\n",
+       "}\n",
+       "#T_489db_row0_col4, #T_489db_row0_col6, #T_489db_row0_col7, #T_489db_row0_col8, #T_489db_row0_col9, #T_489db_row0_col10, #T_489db_row2_col1, #T_489db_row2_col2, #T_489db_row2_col3, #T_489db_row2_col5 {\n",
+       "  background-color: rgba(0, 255, 0, 0.5);\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "#T_489db_row0_col5 {\n",
+       "  background-color: rgba(0, 255, 0, 0.3721843005938857);\n",
+       "}\n",
+       "#T_489db_row0_col11, #T_489db_row0_col12, #T_489db_row0_col13, #T_489db_row0_col14 {\n",
+       "  background-color: rgba(255, 180, 180, 0.5);\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "#T_489db_row1_col1 {\n",
+       "  background-color: rgba(0, 255, 0, 0.2369363386202271);\n",
+       "}\n",
+       "#T_489db_row1_col2 {\n",
+       "  background-color: rgba(0, 255, 0, 0.3273525991163753);\n",
+       "}\n",
+       "#T_489db_row1_col3 {\n",
+       "  background-color: rgba(0, 255, 0, 0.3532274801016337);\n",
+       "}\n",
+       "#T_489db_row1_col4 {\n",
+       "  background-color: rgba(0, 255, 0, 0.36969696969696975);\n",
+       "}\n",
+       "#T_489db_row1_col5 {\n",
+       "  background-color: rgba(0, 255, 0, 0.28773912314821415);\n",
+       "}\n",
+       "#T_489db_row1_col6 {\n",
+       "  background-color: rgba(0, 255, 0, 0.467);\n",
+       "}\n",
+       "#T_489db_row1_col7 {\n",
+       "  background-color: rgba(0, 255, 0, 0.2702702702702701);\n",
+       "}\n",
+       "#T_489db_row1_col8 {\n",
+       "  background-color: rgba(0, 255, 0, 0.44356261022927695);\n",
+       "}\n",
+       "#T_489db_row1_col9 {\n",
+       "  background-color: rgba(0, 255, 0, 0.4252163164400494);\n",
+       "}\n",
+       "#T_489db_row1_col10 {\n",
+       "  background-color: rgba(0, 255, 0, 0.4266666666666667);\n",
+       "}\n",
+       "#T_489db_row1_col11 {\n",
+       "  background-color: rgba(255, 180, 180, 0.4785068842674822);\n",
+       "}\n",
+       "#T_489db_row1_col12 {\n",
+       "  background-color: rgba(255, 180, 180, 0.4859632908330832);\n",
+       "}\n",
+       "#T_489db_row1_col13 {\n",
+       "  background-color: rgba(255, 180, 180, 0.49588477366255146);\n",
+       "}\n",
+       "#T_489db_row1_col14 {\n",
+       "  background-color: rgba(255, 180, 180, 0.45916334661354585);\n",
+       "}\n",
+       "#T_489db_row2_col4 {\n",
+       "  background-color: rgba(0, 255, 0, 0.3954657847231085);\n",
+       "}\n",
+       "#T_489db_row2_col6 {\n",
+       "  background-color: rgba(0, 255, 0, 0.49950110912323);\n",
+       "}\n",
+       "#T_489db_row2_col7 {\n",
+       "  background-color: rgba(0, 255, 0, 0.49339199388349375);\n",
+       "}\n",
+       "#T_489db_row2_col8 {\n",
+       "  background-color: rgba(0, 255, 0, 0.46655078581821774);\n",
+       "}\n",
+       "#T_489db_row2_col9 {\n",
+       "  background-color: rgba(0, 255, 0, 0.08450589398076416);\n",
+       "}\n",
+       "#T_489db_row2_col10 {\n",
+       "  background-color: rgba(0, 255, 0, 0.4025441141481753);\n",
+       "}\n",
+       "#T_489db_row2_col11, #T_489db_row2_col12, #T_489db_row3_col13, #T_489db_row3_col14 {\n",
+       "  background-color: rgba(255, 180, 180, 0.0);\n",
+       "}\n",
+       "#T_489db_row2_col13 {\n",
+       "  background-color: rgba(255, 180, 180, 0.45724737040177293);\n",
+       "}\n",
+       "#T_489db_row2_col14 {\n",
+       "  background-color: rgba(255, 180, 180, 0.0018813700315011839);\n",
+       "}\n",
+       "#T_489db_row3_col1, #T_489db_row3_col2, #T_489db_row3_col3, #T_489db_row3_col4, #T_489db_row3_col5, #T_489db_row3_col6, #T_489db_row3_col7, #T_489db_row3_col8, #T_489db_row3_col9, #T_489db_row3_col10 {\n",
+       "  background-color: rgba(0, 255, 0, 0.0);\n",
+       "}\n",
+       "#T_489db_row3_col11 {\n",
+       "  background-color: rgba(255, 180, 180, 0.01681914082755065);\n",
+       "}\n",
+       "#T_489db_row3_col12 {\n",
+       "  background-color: rgba(255, 180, 180, 0.017133395510392058);\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_1b1dc\">\n",
+       "<table id=\"T_489db\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_1b1dc_level0_col0\" class=\"col_heading level0 col0\" >REPLAN RATE (Hz)</th>\n",
-       "      <th id=\"T_1b1dc_level0_col1\" class=\"col_heading level0 col1\" >LINEAR SPEED (↑)</th>\n",
-       "      <th id=\"T_1b1dc_level0_col2\" class=\"col_heading level0 col2\" >LINEAR ACCEL. (↑)</th>\n",
-       "      <th id=\"T_1b1dc_level0_col3\" class=\"col_heading level0 col3\" >ANG. SPEED (↑)</th>\n",
-       "      <th id=\"T_1b1dc_level0_col4\" class=\"col_heading level0 col4\" >ANG. ACCEL. (↑)</th>\n",
-       "      <th id=\"T_1b1dc_level0_col5\" class=\"col_heading level0 col5\" >DIST. TO OBJ. (↑)</th>\n",
-       "      <th id=\"T_1b1dc_level0_col6\" class=\"col_heading level0 col6\" >COLLISION (↑)</th>\n",
-       "      <th id=\"T_1b1dc_level0_col7\" class=\"col_heading level0 col7\" >TTC (↑)</th>\n",
-       "      <th id=\"T_1b1dc_level0_col8\" class=\"col_heading level0 col8\" >DIST. TO ROAD EDGE (↑)</th>\n",
-       "      <th id=\"T_1b1dc_level0_col9\" class=\"col_heading level0 col9\" >OFFROAD (↑)</th>\n",
-       "      <th id=\"T_1b1dc_level0_col10\" class=\"col_heading level0 col10\" >COMPOSITE METRIC (↑)</th>\n",
-       "      <th id=\"T_1b1dc_level0_col11\" class=\"col_heading level0 col11\" >ADE (↓)</th>\n",
-       "      <th id=\"T_1b1dc_level0_col12\" class=\"col_heading level0 col12\" >MINADE (↓)</th>\n",
-       "      <th id=\"T_1b1dc_level0_col13\" class=\"col_heading level0 col13\" >COLLISION RATE (↓)</th>\n",
-       "      <th id=\"T_1b1dc_level0_col14\" class=\"col_heading level0 col14\" >OFFROAD RATE (↓)</th>\n",
+       "      <th id=\"T_489db_level0_col0\" class=\"col_heading level0 col0\" >REPLAN RATE (Hz)</th>\n",
+       "      <th id=\"T_489db_level0_col1\" class=\"col_heading level0 col1\" >LINEAR SPEED (↑)</th>\n",
+       "      <th id=\"T_489db_level0_col2\" class=\"col_heading level0 col2\" >LINEAR ACCEL. (↑)</th>\n",
+       "      <th id=\"T_489db_level0_col3\" class=\"col_heading level0 col3\" >ANG. SPEED (↑)</th>\n",
+       "      <th id=\"T_489db_level0_col4\" class=\"col_heading level0 col4\" >ANG. ACCEL. (↑)</th>\n",
+       "      <th id=\"T_489db_level0_col5\" class=\"col_heading level0 col5\" >DIST. TO OBJ. (↑)</th>\n",
+       "      <th id=\"T_489db_level0_col6\" class=\"col_heading level0 col6\" >COLLISION (↑)</th>\n",
+       "      <th id=\"T_489db_level0_col7\" class=\"col_heading level0 col7\" >TTC (↑)</th>\n",
+       "      <th id=\"T_489db_level0_col8\" class=\"col_heading level0 col8\" >DIST. TO ROAD EDGE (↑)</th>\n",
+       "      <th id=\"T_489db_level0_col9\" class=\"col_heading level0 col9\" >OFFROAD (↑)</th>\n",
+       "      <th id=\"T_489db_level0_col10\" class=\"col_heading level0 col10\" >COMPOSITE METRIC (↑)</th>\n",
+       "      <th id=\"T_489db_level0_col11\" class=\"col_heading level0 col11\" >ADE (↓)</th>\n",
+       "      <th id=\"T_489db_level0_col12\" class=\"col_heading level0 col12\" >MINADE (↓)</th>\n",
+       "      <th id=\"T_489db_level0_col13\" class=\"col_heading level0 col13\" >COLLISION RATE (↓)</th>\n",
+       "      <th id=\"T_489db_level0_col14\" class=\"col_heading level0 col14\" >OFFROAD RATE (↓)</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th class=\"index_name level0\" >AGENT POLICY</th>\n",
@@ -172,82 +287,82 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_1b1dc_level0_row0\" class=\"row_heading level0 row0\" >Logged oracle</th>\n",
-       "      <td id=\"T_1b1dc_row0_col0\" class=\"data row0 col0\" >-</td>\n",
-       "      <td id=\"T_1b1dc_row0_col1\" class=\"data row0 col1\" >0.476</td>\n",
-       "      <td id=\"T_1b1dc_row0_col2\" class=\"data row0 col2\" >0.478</td>\n",
-       "      <td id=\"T_1b1dc_row0_col3\" class=\"data row0 col3\" >0.578</td>\n",
-       "      <td id=\"T_1b1dc_row0_col4\" class=\"data row0 col4\" >0.694</td>\n",
-       "      <td id=\"T_1b1dc_row0_col5\" class=\"data row0 col5\" >0.476</td>\n",
-       "      <td id=\"T_1b1dc_row0_col6\" class=\"data row0 col6\" >1.000</td>\n",
-       "      <td id=\"T_1b1dc_row0_col7\" class=\"data row0 col7\" >0.883</td>\n",
-       "      <td id=\"T_1b1dc_row0_col8\" class=\"data row0 col8\" >0.715</td>\n",
-       "      <td id=\"T_1b1dc_row0_col9\" class=\"data row0 col9\" >1.000</td>\n",
-       "      <td id=\"T_1b1dc_row0_col10\" class=\"data row0 col10\" >0.819</td>\n",
-       "      <td id=\"T_1b1dc_row0_col11\" class=\"data row0 col11\" >0.000</td>\n",
-       "      <td id=\"T_1b1dc_row0_col12\" class=\"data row0 col12\" >0.000</td>\n",
-       "      <td id=\"T_1b1dc_row0_col13\" class=\"data row0 col13\" >0.028</td>\n",
-       "      <td id=\"T_1b1dc_row0_col14\" class=\"data row0 col14\" >0.111</td>\n",
+       "      <th id=\"T_489db_level0_row0\" class=\"row_heading level0 row0\" >Logged oracle</th>\n",
+       "      <td id=\"T_489db_row0_col0\" class=\"data row0 col0\" >-</td>\n",
+       "      <td id=\"T_489db_row0_col1\" class=\"data row0 col1\" >0.476</td>\n",
+       "      <td id=\"T_489db_row0_col2\" class=\"data row0 col2\" >0.478</td>\n",
+       "      <td id=\"T_489db_row0_col3\" class=\"data row0 col3\" >0.578</td>\n",
+       "      <td id=\"T_489db_row0_col4\" class=\"data row0 col4\" >0.694</td>\n",
+       "      <td id=\"T_489db_row0_col5\" class=\"data row0 col5\" >0.476</td>\n",
+       "      <td id=\"T_489db_row0_col6\" class=\"data row0 col6\" >1.000</td>\n",
+       "      <td id=\"T_489db_row0_col7\" class=\"data row0 col7\" >0.883</td>\n",
+       "      <td id=\"T_489db_row0_col8\" class=\"data row0 col8\" >0.715</td>\n",
+       "      <td id=\"T_489db_row0_col9\" class=\"data row0 col9\" >1.000</td>\n",
+       "      <td id=\"T_489db_row0_col10\" class=\"data row0 col10\" >0.819</td>\n",
+       "      <td id=\"T_489db_row0_col11\" class=\"data row0 col11\" >0.000</td>\n",
+       "      <td id=\"T_489db_row0_col12\" class=\"data row0 col12\" >0.000</td>\n",
+       "      <td id=\"T_489db_row0_col13\" class=\"data row0 col13\" >0.028</td>\n",
+       "      <td id=\"T_489db_row0_col14\" class=\"data row0 col14\" >0.111</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1b1dc_level0_row1\" class=\"row_heading level0 row1\" >Guided self-play (ours)</th>\n",
-       "      <td id=\"T_1b1dc_row1_col0\" class=\"data row1 col0\" >10</td>\n",
-       "      <td id=\"T_1b1dc_row1_col1\" class=\"data row1 col1\" >0.589</td>\n",
-       "      <td id=\"T_1b1dc_row1_col2\" class=\"data row1 col2\" >0.366</td>\n",
-       "      <td id=\"T_1b1dc_row1_col3\" class=\"data row1 col3\" >0.629</td>\n",
-       "      <td id=\"T_1b1dc_row1_col4\" class=\"data row1 col4\" >0.446</td>\n",
-       "      <td id=\"T_1b1dc_row1_col5\" class=\"data row1 col5\" >0.376</td>\n",
-       "      <td id=\"T_1b1dc_row1_col6\" class=\"data row1 col6\" >0.999</td>\n",
-       "      <td id=\"T_1b1dc_row1_col7\" class=\"data row1 col7\" >0.989</td>\n",
-       "      <td id=\"T_1b1dc_row1_col8\" class=\"data row1 col8\" >0.531</td>\n",
-       "      <td id=\"T_1b1dc_row1_col9\" class=\"data row1 col9\" >0.999</td>\n",
-       "      <td id=\"T_1b1dc_row1_col10\" class=\"data row1 col10\" >0.791</td>\n",
-       "      <td id=\"T_1b1dc_row1_col11\" class=\"data row1 col11\" >0.873</td>\n",
-       "      <td id=\"T_1b1dc_row1_col12\" class=\"data row1 col12\" >0.873</td>\n",
-       "      <td id=\"T_1b1dc_row1_col13\" class=\"data row1 col13\" >0.000</td>\n",
-       "      <td id=\"T_1b1dc_row1_col14\" class=\"data row1 col14\" >0.000</td>\n",
+       "      <th id=\"T_489db_level0_row1\" class=\"row_heading level0 row1\" >VBD</th>\n",
+       "      <td id=\"T_489db_row1_col0\" class=\"data row1 col0\" >0.125</td>\n",
+       "      <td id=\"T_489db_row1_col1\" class=\"data row1 col1\" >0.359</td>\n",
+       "      <td id=\"T_489db_row1_col2\" class=\"data row1 col2\" >0.366</td>\n",
+       "      <td id=\"T_489db_row1_col3\" class=\"data row1 col3\" >0.420</td>\n",
+       "      <td id=\"T_489db_row1_col4\" class=\"data row1 col4\" >0.522</td>\n",
+       "      <td id=\"T_489db_row1_col5\" class=\"data row1 col5\" >0.368</td>\n",
+       "      <td id=\"T_489db_row1_col6\" class=\"data row1 col6\" >0.934</td>\n",
+       "      <td id=\"T_489db_row1_col7\" class=\"data row1 col7\" >0.815</td>\n",
+       "      <td id=\"T_489db_row1_col8\" class=\"data row1 col8\" >0.651</td>\n",
+       "      <td id=\"T_489db_row1_col9\" class=\"data row1 col9\" >0.879</td>\n",
+       "      <td id=\"T_489db_row1_col10\" class=\"data row1 col10\" >0.720</td>\n",
+       "      <td id=\"T_489db_row1_col11\" class=\"data row1 col11\" >2.257</td>\n",
+       "      <td id=\"T_489db_row1_col12\" class=\"data row1 col12\" >1.474</td>\n",
+       "      <td id=\"T_489db_row1_col13\" class=\"data row1 col13\" >0.036</td>\n",
+       "      <td id=\"T_489db_row1_col14\" class=\"data row1 col14\" >0.152</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1b1dc_level0_row2\" class=\"row_heading level0 row2\" >VBD</th>\n",
-       "      <td id=\"T_1b1dc_row2_col0\" class=\"data row2 col0\" >0.125</td>\n",
-       "      <td id=\"T_1b1dc_row2_col1\" class=\"data row2 col1\" >0.359</td>\n",
-       "      <td id=\"T_1b1dc_row2_col2\" class=\"data row2 col2\" >0.366</td>\n",
-       "      <td id=\"T_1b1dc_row2_col3\" class=\"data row2 col3\" >0.420</td>\n",
-       "      <td id=\"T_1b1dc_row2_col4\" class=\"data row2 col4\" >0.522</td>\n",
-       "      <td id=\"T_1b1dc_row2_col5\" class=\"data row2 col5\" >0.368</td>\n",
-       "      <td id=\"T_1b1dc_row2_col6\" class=\"data row2 col6\" >0.934</td>\n",
-       "      <td id=\"T_1b1dc_row2_col7\" class=\"data row2 col7\" >0.815</td>\n",
-       "      <td id=\"T_1b1dc_row2_col8\" class=\"data row2 col8\" >0.651</td>\n",
-       "      <td id=\"T_1b1dc_row2_col9\" class=\"data row2 col9\" >0.879</td>\n",
-       "      <td id=\"T_1b1dc_row2_col10\" class=\"data row2 col10\" >0.720</td>\n",
-       "      <td id=\"T_1b1dc_row2_col11\" class=\"data row2 col11\" >2.257</td>\n",
-       "      <td id=\"T_1b1dc_row2_col12\" class=\"data row2 col12\" >1.474</td>\n",
-       "      <td id=\"T_1b1dc_row2_col13\" class=\"data row2 col13\" >0.036</td>\n",
-       "      <td id=\"T_1b1dc_row2_col14\" class=\"data row2 col14\" >0.152</td>\n",
+       "      <th id=\"T_489db_level0_row2\" class=\"row_heading level0 row2\" >Guided self-play (ours)</th>\n",
+       "      <td id=\"T_489db_row2_col0\" class=\"data row2 col0\" >10</td>\n",
+       "      <td id=\"T_489db_row2_col1\" class=\"data row2 col1\" >0.755</td>\n",
+       "      <td id=\"T_489db_row2_col2\" class=\"data row2 col2\" >0.498</td>\n",
+       "      <td id=\"T_489db_row2_col3\" class=\"data row2 col3\" >0.589</td>\n",
+       "      <td id=\"T_489db_row2_col4\" class=\"data row2 col4\" >0.556</td>\n",
+       "      <td id=\"T_489db_row2_col5\" class=\"data row2 col5\" >0.639</td>\n",
+       "      <td id=\"T_489db_row2_col6\" class=\"data row2 col6\" >0.999</td>\n",
+       "      <td id=\"T_489db_row2_col7\" class=\"data row2 col7\" >0.881</td>\n",
+       "      <td id=\"T_489db_row2_col8\" class=\"data row2 col8\" >0.677</td>\n",
+       "      <td id=\"T_489db_row2_col9\" class=\"data row2 col9\" >0.328</td>\n",
+       "      <td id=\"T_489db_row2_col10\" class=\"data row2 col10\" >0.687</td>\n",
+       "      <td id=\"T_489db_row2_col11\" class=\"data row2 col11\" >52.505</td>\n",
+       "      <td id=\"T_489db_row2_col12\" class=\"data row2 col12\" >52.505</td>\n",
+       "      <td id=\"T_489db_row2_col13\" class=\"data row2 col13\" >0.111</td>\n",
+       "      <td id=\"T_489db_row2_col14\" class=\"data row2 col14\" >0.611</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1b1dc_level0_row3\" class=\"row_heading level0 row3\" >Random agent</th>\n",
-       "      <td id=\"T_1b1dc_row3_col0\" class=\"data row3 col0\" >10</td>\n",
-       "      <td id=\"T_1b1dc_row3_col1\" class=\"data row3 col1\" >0.002</td>\n",
-       "      <td id=\"T_1b1dc_row3_col2\" class=\"data row3 col2\" >0.116</td>\n",
-       "      <td id=\"T_1b1dc_row3_col3\" class=\"data row3 col3\" >0.014</td>\n",
-       "      <td id=\"T_1b1dc_row3_col4\" class=\"data row3 col4\" >0.034</td>\n",
-       "      <td id=\"T_1b1dc_row3_col5\" class=\"data row3 col5\" >0.000</td>\n",
-       "      <td id=\"T_1b1dc_row3_col6\" class=\"data row3 col6\" >0.000</td>\n",
-       "      <td id=\"T_1b1dc_row3_col7\" class=\"data row3 col7\" >0.735</td>\n",
-       "      <td id=\"T_1b1dc_row3_col8\" class=\"data row3 col8\" >0.148</td>\n",
-       "      <td id=\"T_1b1dc_row3_col9\" class=\"data row3 col9\" >0.191</td>\n",
-       "      <td id=\"T_1b1dc_row3_col10\" class=\"data row3 col10\" >0.144</td>\n",
-       "      <td id=\"T_1b1dc_row3_col11\" class=\"data row3 col11\" >50.739</td>\n",
-       "      <td id=\"T_1b1dc_row3_col12\" class=\"data row3 col12\" >50.706</td>\n",
-       "      <td id=\"T_1b1dc_row3_col13\" class=\"data row3 col13\" >1.000</td>\n",
-       "      <td id=\"T_1b1dc_row3_col14\" class=\"data row3 col14\" >0.613</td>\n",
+       "      <th id=\"T_489db_level0_row3\" class=\"row_heading level0 row3\" >Random agent</th>\n",
+       "      <td id=\"T_489db_row3_col0\" class=\"data row3 col0\" >10</td>\n",
+       "      <td id=\"T_489db_row3_col1\" class=\"data row3 col1\" >0.002</td>\n",
+       "      <td id=\"T_489db_row3_col2\" class=\"data row3 col2\" >0.116</td>\n",
+       "      <td id=\"T_489db_row3_col3\" class=\"data row3 col3\" >0.014</td>\n",
+       "      <td id=\"T_489db_row3_col4\" class=\"data row3 col4\" >0.034</td>\n",
+       "      <td id=\"T_489db_row3_col5\" class=\"data row3 col5\" >0.000</td>\n",
+       "      <td id=\"T_489db_row3_col6\" class=\"data row3 col6\" >0.000</td>\n",
+       "      <td id=\"T_489db_row3_col7\" class=\"data row3 col7\" >0.735</td>\n",
+       "      <td id=\"T_489db_row3_col8\" class=\"data row3 col8\" >0.148</td>\n",
+       "      <td id=\"T_489db_row3_col9\" class=\"data row3 col9\" >0.191</td>\n",
+       "      <td id=\"T_489db_row3_col10\" class=\"data row3 col10\" >0.144</td>\n",
+       "      <td id=\"T_489db_row3_col11\" class=\"data row3 col11\" >50.739</td>\n",
+       "      <td id=\"T_489db_row3_col12\" class=\"data row3 col12\" >50.706</td>\n",
+       "      <td id=\"T_489db_row3_col13\" class=\"data row3 col13\" >1.000</td>\n",
+       "      <td id=\"T_489db_row3_col14\" class=\"data row3 col14\" >0.613</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f4ee0408e90>"
+       "<pandas.io.formats.style.Styler at 0x7f8375366010>"
       ]
      },
      "metadata": {},
@@ -259,6 +374,20 @@
     "display(table);"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 30,
diff --git a/examples/experimental/notebooks/debug.ipynb b/examples/experimental/notebooks/debug.ipynb
index f4fd484c2..9e00a48e0 100644
--- a/examples/experimental/notebooks/debug.ipynb
+++ b/examples/experimental/notebooks/debug.ipynb
@@ -19,14 +19,74 @@
     "import torch\n",
     "from PIL import Image\n",
     "\n",
-    "from matplotlib import pyplot as plt"
+    "from matplotlib import pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set_style(\"ticks\", rc={\"figure.facecolor\": \"none\", \"axes.facecolor\": \"none\"})\n",
+    "%config InlineBackend.figure_format = 'svg'\n",
+    "\n",
+    "plt.set_loglevel(\"WARNING\")\n",
+    "%matplotlib inline"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 2,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "### Make environment"
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "\n",
+    "def plot_agent_guidance_info(guidance_obs, center_agent_idx):\n",
+    " \n",
+    "    agent_guidance = guidance_obs[center_agent_idx, :, :]\n",
+    "    \n",
+    "    time_steps = np.arange(agent_guidance.shape[0])\n",
+    "    \n",
+    "    fig, axes = plt.subplots(1, 3, figsize=(10, 3), sharex=True, sharey=True)\n",
+    "    \n",
+    "    # Find where the one-hot encoding is 1 (next timestep indicator)\n",
+    "    next_timestep_idx = np.where(agent_guidance[:, 3] == 1)[0][0]\n",
+    "    \n",
+    "    axes[0].plot(time_steps, agent_guidance[:, 0], label='x-position')\n",
+    "    axes[0].plot(time_steps, agent_guidance[:, 1], label='y-position')\n",
+    "    \n",
+    "    axes[0].scatter(time_steps[next_timestep_idx], agent_guidance[next_timestep_idx, 0], color='purple', marker='^', s=100, zorder=5, label='_nolegend_')\n",
+    "    axes[0].scatter(time_steps[next_timestep_idx], agent_guidance[next_timestep_idx, 1], color='purple', marker='^', s=100, zorder=5, label='_nolegend_')\n",
+    "    \n",
+    "    axes[0].set_xlabel('Time')\n",
+    "    axes[0].set_ylabel('Suggested position (x,y)')\n",
+    "    axes[0].legend()\n",
+    "    axes[0].grid(True, alpha=0.3)\n",
+    "    \n",
+    "    axes[1].plot(time_steps, agent_guidance[:, 2])\n",
+    "    \n",
+    "\n",
+    "    axes[1].scatter(time_steps[next_timestep_idx], agent_guidance[next_timestep_idx, 2], color='purple', marker='^', s=100, zorder=5)\n",
+    "    \n",
+    "    axes[1].set_xlabel('Time')\n",
+    "    axes[1].set_ylabel('Suggested speed')\n",
+    "    axes[1].grid(True, alpha=0.3)\n",
+    "    \n",
+    "    axes[2].plot(time_steps, agent_guidance[:, 4])\n",
+    "    axes[2].scatter(time_steps[next_timestep_idx], agent_guidance[next_timestep_idx, 4], color='purple', marker='^', s=100, zorder=5)\n",
+    "    \n",
+    "    axes[2].set_xlabel('Time')\n",
+    "    axes[2].set_ylabel('Suggested heading (rad)')\n",
+    "    axes[2].set_title('Heading')\n",
+    "    axes[2].grid(True, alpha=0.3)\n",
+    "    \n",
+    "    fig.suptitle(f\"Agent's {center_agent_idx} normalized guidance information\")\n",
+    "    \n",
+    "    # Add a unified legend for the purple triangle\n",
+    "    triangle_patch = plt.Line2D([0], [0], marker='^', color='w', markerfacecolor='purple')\n",
+    "    fig.legend([triangle_patch], ['Next timestep indicator'], loc='lower center', bbox_to_anchor=(0.53, -0.15), facecolor='white')\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.subplots_adjust(top=0.85, bottom=0.15)\n",
+    "    \n",
+    "    return fig"
    ]
   },
   {
@@ -34,22 +94,51 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['/home/emerge/gpudrive/data/processed/wosac/debug/1ce3b38fe5cd2597.json']\n"
+     ]
+    }
+   ],
    "source": [
     "env_config = EnvConfig(\n",
     "    dynamics_model=\"classic\",\n",
     "    reward_type=\"guided_autonomy\",\n",
-    "    add_reference_path=True,\n",
+    "    guidance=True,\n",
+    "    add_reference_heading=True,\n",
+    "    add_reference_speed=True,\n",
+    "    add_reference_pos_xy=True,\n",
+    "    init_mode=\"womd_tracks_to_predict\"\n",
     ")\n",
     "render_config = RenderConfig()\n",
     "\n",
     "train_loader = SceneDataLoader(\n",
-    "    root=\"data/processed/examples\",\n",
-    "    batch_size=2,\n",
+    "    root=\"/home/emerge/gpudrive/data/processed/wosac/debug\",\n",
+    "    batch_size=1,\n",
     "    dataset_size=100,\n",
-    "    sample_with_replacement=True,\n",
+    "    sample_with_replacement=False,\n",
     "    shuffle=False,\n",
+    "    file_prefix=\"\",\n",
     ")\n",
     "\n",
+    "print(train_loader.dataset)\n",
+    "\n",
     "env = GPUDriveTorchEnv(\n",
     "    config=env_config,\n",
     "    data_loader=train_loader,\n",
@@ -60,58 +149,32349 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Controllable agents: tensor([ 0,  1,  2,  4, 12])\n",
-      "Controlling agent 2 in env 0.\n"
+      "Controllable agents: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+      "        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+      "        36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51])\n",
+      "Controlling agent 12 in env 0.\n"
      ]
     }
    ],
    "source": [
-    "env_i = 0\n",
-    "agent_j = 2\n",
+    "env_idx = 0\n",
+    "center_agent_idx = 12\n",
     "\n",
     "control_mask = env.cont_agent_mask\n",
     "\n",
-    "print(f'Controllable agents: {torch.where(env.cont_agent_mask[env_i, :])[0]}')\n",
-    "\n",
-    "print(f'Controlling agent {agent_j} in env {env_i}.')"
+    "print(f'Controllable agents: {torch.where(env.cont_agent_mask[env_idx, :])[0]}')\n",
+    "print(f'Controlling agent {center_agent_idx} in env {env_idx}.')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Verify indexing"
+    "### Step env"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "obs = env.reset(control_mask)"
+    "obs = env.reset(control_mask)\n",
+    "\n",
+    "expert_actions, _, _, _ = env.get_expert_actions()\n",
+    "\n",
+    "# for t in range(1):\n",
+    "#     env.step_dynamics(expert_actions[:, :, t, :])\n",
+    "#     obs = env.get_obs(control_mask)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Show agent guidance information \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
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+       "   <cc:Work>\n",
+       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
+       "    <dc:date>2025-04-28T13:02:40.397071</dc:date>\n",
+       "    <dc:format>image/svg+xml</dc:format>\n",
+       "    <dc:creator>\n",
+       "     <cc:Agent>\n",
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+       "     </cc:Agent>\n",
+       "    </dc:creator>\n",
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+       "  </rdf:RDF>\n",
+       " </metadata>\n",
+       " <defs>\n",
+       "  <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
+       " </defs>\n",
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+      ],
+      "text/plain": [
+       "<Figure size 1000x300 with 3 Axes>"
+      ]
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+      "needs_background": "light"
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+     "output_type": "display_data"
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+   ],
    "source": [
-    "obs = env.reset(control_mask)\n",
+    "for center_agent_idx in [0, 3, 12]:\n",
+    "    plot_agent_guidance_info(env.guidance_obs, center_agent_idx)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Show corresponding full agent views"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
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+    "center_agent_idx=3\n",
     "\n",
-    "expert_actions, _, _, _ = env.get_expert_actions()\n",
+    "traj_masked = env.reference_path[center_agent_idx, :, :]\n",
     "\n",
-    "for t in range(5):\n",
-    "    env.step_dynamics(expert_actions[:, :, t, :])\n",
-    "    obs = env.get_obs(control_mask)"
+    "agent_obs_masked = env.vis.plot_agent_observation(\n",
+    "    env_idx=env_idx,\n",
+    "    agent_idx=center_agent_idx,\n",
+    "    figsize=(8, 8),\n",
+    "    trajectory=traj_masked,\n",
+    ")"
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+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
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+   "source": [
+    "center_agent_idx=40\n",
+    "\n",
+    "traj_masked = env.reference_path[center_agent_idx, :, :]\n",
+    "\n",
+    "agent_obs_masked = env.vis.plot_agent_observation(\n",
+    "    env_idx=env_idx,\n",
+    "    agent_idx=center_agent_idx,\n",
+    "    figsize=(8, 8),\n",
+    "    trajectory=traj_masked,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simulator state centered at that agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
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+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sim_states = env.vis.plot_simulator_state(\n",
+    "    env_indices=env_idx,\n",
+    "    zoom_radius=100,\n",
+    "    plot_guidance_pos_xy=True,\n",
+    "    center_agent_indices=[center_agent_idx],\n",
+    ")\n",
+    "#Image.fromarray(img_from_fig(sim_states[0]))\n",
+    "sim_states[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Verify indexing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obs = env.reset(control_mask)"
    ]
   },
   {
@@ -181,116 +32561,6 @@
     "Image.fromarray(img_from_fig(agent_obs_masked))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[-2.8176e+00,  1.6540e-06,  0.0000e+00],\n",
-       "        [-2.2567e+00, -5.4991e-02,  0.0000e+00],\n",
-       "        [-1.6712e+00, -1.1899e-01,  0.0000e+00],\n",
-       "        [-1.1435e+00, -2.1718e-01,  0.0000e+00],\n",
-       "        [-5.8799e-01, -3.4425e-01,  0.0000e+00],\n",
-       "        [-4.9307e-02, -4.9633e-01,  0.0000e+00],\n",
-       "        [ 4.9877e-01, -6.5314e-01,  1.0000e+00],\n",
-       "        [ 1.0278e+00, -8.2594e-01,  0.0000e+00],\n",
-       "        [ 1.5701e+00, -1.0161e+00,  0.0000e+00],\n",
-       "        [ 2.0863e+00, -1.2118e+00,  0.0000e+00],\n",
-       "        [ 2.6098e+00, -1.4267e+00,  0.0000e+00],\n",
-       "        [ 3.1191e+00, -1.6454e+00,  0.0000e+00],\n",
-       "        [ 3.6540e+00, -1.8755e+00,  0.0000e+00],\n",
-       "        [ 4.1953e+00, -2.1215e+00,  0.0000e+00],\n",
-       "        [ 4.7675e+00, -2.3735e+00,  0.0000e+00],\n",
-       "        [ 5.3208e+00, -2.6513e+00,  0.0000e+00],\n",
-       "        [ 5.8825e+00, -2.8995e+00,  0.0000e+00],\n",
-       "        [ 6.4127e+00, -3.1944e+00,  0.0000e+00],\n",
-       "        [ 6.9735e+00, -3.4876e+00,  0.0000e+00],\n",
-       "        [ 7.5203e+00, -3.8051e+00,  0.0000e+00],\n",
-       "        [ 8.0950e+00, -4.1299e+00,  0.0000e+00],\n",
-       "        [ 8.6821e+00, -4.4582e+00,  0.0000e+00],\n",
-       "        [ 9.2876e+00, -4.8013e+00,  0.0000e+00],\n",
-       "        [ 9.9100e+00, -5.1523e+00,  0.0000e+00],\n",
-       "        [ 1.0536e+01, -5.5163e+00,  0.0000e+00],\n",
-       "        [ 1.1209e+01, -5.8927e+00,  0.0000e+00],\n",
-       "        [ 1.1828e+01, -6.2615e+00,  0.0000e+00],\n",
-       "        [ 1.2506e+01, -6.6497e+00,  0.0000e+00],\n",
-       "        [ 1.3193e+01, -7.0484e+00,  0.0000e+00],\n",
-       "        [ 1.3869e+01, -7.4457e+00,  0.0000e+00],\n",
-       "        [ 1.4562e+01, -7.8596e+00,  0.0000e+00],\n",
-       "        [ 1.5258e+01, -8.2762e+00,  0.0000e+00],\n",
-       "        [ 1.5968e+01, -8.6956e+00,  0.0000e+00],\n",
-       "        [ 1.6693e+01, -9.1250e+00,  0.0000e+00],\n",
-       "        [ 1.7441e+01, -9.5761e+00,  0.0000e+00],\n",
-       "        [ 1.8156e+01, -9.9870e+00,  0.0000e+00],\n",
-       "        [ 1.8938e+01, -1.0467e+01,  0.0000e+00],\n",
-       "        [ 1.9656e+01, -1.0894e+01,  0.0000e+00],\n",
-       "        [ 2.0452e+01, -1.1372e+01,  0.0000e+00],\n",
-       "        [ 2.1205e+01, -1.1824e+01,  0.0000e+00],\n",
-       "        [ 2.1998e+01, -1.2283e+01,  0.0000e+00],\n",
-       "        [ 2.2798e+01, -1.2767e+01,  0.0000e+00],\n",
-       "        [ 2.3605e+01, -1.3238e+01,  0.0000e+00],\n",
-       "        [ 2.4404e+01, -1.3717e+01,  0.0000e+00],\n",
-       "        [ 2.5225e+01, -1.4205e+01,  0.0000e+00],\n",
-       "        [ 2.6044e+01, -1.4694e+01,  0.0000e+00],\n",
-       "        [ 2.6874e+01, -1.5197e+01,  0.0000e+00],\n",
-       "        [ 2.7730e+01, -1.5706e+01,  0.0000e+00],\n",
-       "        [ 2.8580e+01, -1.6209e+01,  0.0000e+00],\n",
-       "        [ 2.9454e+01, -1.6734e+01,  0.0000e+00],\n",
-       "        [ 3.0307e+01, -1.7237e+01,  0.0000e+00],\n",
-       "        [ 3.1191e+01, -1.7769e+01,  0.0000e+00],\n",
-       "        [ 3.2098e+01, -1.8292e+01,  0.0000e+00],\n",
-       "        [ 3.2996e+01, -1.8836e+01,  0.0000e+00],\n",
-       "        [ 3.3914e+01, -1.9388e+01,  0.0000e+00],\n",
-       "        [ 3.4817e+01, -1.9921e+01,  0.0000e+00],\n",
-       "        [ 3.5705e+01, -2.0455e+01,  0.0000e+00],\n",
-       "        [ 3.6645e+01, -2.0996e+01,  0.0000e+00],\n",
-       "        [ 3.7594e+01, -2.1556e+01,  0.0000e+00],\n",
-       "        [ 3.8527e+01, -2.2106e+01,  0.0000e+00],\n",
-       "        [ 3.9451e+01, -2.2661e+01,  0.0000e+00],\n",
-       "        [ 4.0428e+01, -2.3217e+01,  0.0000e+00],\n",
-       "        [ 4.1375e+01, -2.3780e+01,  0.0000e+00],\n",
-       "        [ 4.2307e+01, -2.4312e+01,  0.0000e+00],\n",
-       "        [ 4.3282e+01, -2.4874e+01,  0.0000e+00],\n",
-       "        [ 4.4235e+01, -2.5452e+01,  0.0000e+00],\n",
-       "        [ 4.5213e+01, -2.6012e+01,  0.0000e+00],\n",
-       "        [ 4.6172e+01, -2.6576e+01,  0.0000e+00],\n",
-       "        [ 4.7116e+01, -2.7120e+01,  0.0000e+00],\n",
-       "        [ 4.8107e+01, -2.7662e+01,  0.0000e+00],\n",
-       "        [ 4.9131e+01, -2.8236e+01,  0.0000e+00],\n",
-       "        [ 5.0127e+01, -2.8802e+01,  0.0000e+00],\n",
-       "        [ 5.1111e+01, -2.9381e+01,  0.0000e+00],\n",
-       "        [ 5.2130e+01, -2.9930e+01,  0.0000e+00],\n",
-       "        [ 5.3149e+01, -3.0526e+01,  0.0000e+00],\n",
-       "        [ 5.4187e+01, -3.1101e+01,  0.0000e+00],\n",
-       "        [ 5.5308e+01, -3.1733e+01,  0.0000e+00],\n",
-       "        [ 5.6316e+01, -3.2302e+01,  0.0000e+00],\n",
-       "        [ 5.7350e+01, -3.2853e+01,  0.0000e+00],\n",
-       "        [ 5.8433e+01, -3.3481e+01,  0.0000e+00],\n",
-       "        [ 5.9471e+01, -3.4043e+01,  0.0000e+00],\n",
-       "        [ 6.0508e+01, -3.4615e+01,  0.0000e+00],\n",
-       "        [ 6.1611e+01, -3.5201e+01,  0.0000e+00],\n",
-       "        [ 6.2707e+01, -3.5811e+01,  0.0000e+00],\n",
-       "        [ 6.3779e+01, -3.6378e+01,  0.0000e+00],\n",
-       "        [ 6.4890e+01, -3.7002e+01,  0.0000e+00],\n",
-       "        [ 6.5991e+01, -3.7572e+01,  0.0000e+00],\n",
-       "        [-2.3981e+01,  1.1701e+01,  0.0000e+00],\n",
-       "        [-2.3981e+01,  1.1701e+01,  0.0000e+00],\n",
-       "        [-2.3981e+01,  1.1701e+01,  0.0000e+00],\n",
-       "        [-2.3981e+01,  1.1701e+01,  0.0000e+00]])"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "traj_masked"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -357,119 +32627,6 @@
    "metadata": {},
    "source": []
   },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[    -4.6843,     -0.0000],\n",
-       "        [    -3.7478,      0.0228],\n",
-       "        [    -2.6972,      0.0244],\n",
-       "        [    -1.6492,      0.0099],\n",
-       "        [    -0.5689,     -0.0026],\n",
-       "        [     0.5339,     -0.0228],\n",
-       "        [     1.6032,     -0.0322],\n",
-       "        [     2.7434,     -0.0402],\n",
-       "        [     3.8651,     -0.0452],\n",
-       "        [     5.0059,     -0.0523],\n",
-       "        [     6.1329,     -0.0523],\n",
-       "        [     7.3169,     -0.0725],\n",
-       "        [     8.4740,     -0.0730],\n",
-       "        [     9.6554,     -0.0751],\n",
-       "        [    10.8401,     -0.0971],\n",
-       "        [    12.0179,     -0.1008],\n",
-       "        [    13.1972,     -0.1081],\n",
-       "        [    14.4008,     -0.1100],\n",
-       "        [    15.6533,     -0.1187],\n",
-       "        [    16.8722,     -0.1357],\n",
-       "        [    18.1067,     -0.1230],\n",
-       "        [    19.3222,     -0.1372],\n",
-       "        [    20.5441,     -0.1466],\n",
-       "        [    21.8482,     -0.1608],\n",
-       "        [    23.0581,     -0.1674],\n",
-       "        [    24.3297,     -0.1660],\n",
-       "        [    25.6302,     -0.1674],\n",
-       "        [    26.8703,     -0.1745],\n",
-       "        [    28.1802,     -0.1851],\n",
-       "        [    29.4575,     -0.1919],\n",
-       "        [    30.7170,     -0.1969],\n",
-       "        [    32.0253,     -0.1917],\n",
-       "        [    33.3079,     -0.2276],\n",
-       "        [    34.6077,     -0.2223],\n",
-       "        [    35.9107,     -0.2487],\n",
-       "        [    37.1519,     -0.2415],\n",
-       "        [    38.4949,     -0.2594],\n",
-       "        [    39.8145,     -0.2651],\n",
-       "        [    41.1175,     -0.2745],\n",
-       "        [    42.5277,     -0.2947],\n",
-       "        [    43.9141,     -0.3020],\n",
-       "        [    45.6483,     -0.3042],\n",
-       "        [    46.7122,     -0.3789],\n",
-       "        [    47.9882,     -0.3708],\n",
-       "        [    49.2079,     -0.4041],\n",
-       "        [    50.4362,     -0.4111],\n",
-       "        [    51.7675,     -0.3951],\n",
-       "        [    53.1219,     -0.4074],\n",
-       "        [    54.4872,     -0.3983],\n",
-       "        [    55.8000,     -0.4567],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000],\n",
-       "        [    -0.0000,     -0.0000]])"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Set print options to suppress scientific notation\n",
-    "torch.set_printoptions(sci_mode=False)\n",
-    "\n",
-    "traj_masked"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 83,
diff --git a/gpudrive/datatypes/observation.py b/gpudrive/datatypes/observation.py
index 8f4208307..e6b98b9e4 100644
--- a/gpudrive/datatypes/observation.py
+++ b/gpudrive/datatypes/observation.py
@@ -290,7 +290,7 @@ def shape(self) -> tuple[int, ...]:
 @dataclass
 class LidarObs:
     """Dataclass representing the scenario view through LiDAR sensors.
-        - Shape: (num_worlds, num_agents, 3, num_lidar_points, 4).
+        - Shape: (num_worlds, num_agents, 3, num_lidar_samples, 4).
         - Axis 2 represents the agent samples, road edge samples, and road line samples.
         - Axis 3 represents the lidar points per type, which can be configured in src/consts.hpp as `numLidarSamples`.
         - Axis 4 represents the depth, type and x, y, values of the lidar points.
@@ -316,9 +316,14 @@ def from_tensor(
         elif backend == "jax":
             raise NotImplementedError("JAX backend not implemented yet.")
 
+    @property
+    def num_lidar_samples(self) -> int:
+        """Number of lidar samples per type."""
+        return self.all_lidar_samples.shape[3]
+
     @property
     def shape(self) -> tuple[int, ...]:
-        """Shape: (num_worlds, num_agents, 3, num_lidar_points, 4)."""
+        """Shape: (num_worlds, num_agents, 3, num_lidar_samples, 4)."""
         return self.all_lidar_samples.shape
 
 
diff --git a/gpudrive/env/env_torch.py b/gpudrive/env/env_torch.py
index cdafc62ca..59e1014e0 100755
--- a/gpudrive/env/env_torch.py
+++ b/gpudrive/env/env_torch.py
@@ -1032,7 +1032,9 @@ def _get_guidance(self, mask=None) -> torch.Tensor:
                 ] = constants.INVALID_ID
                 guidance.append(reference_headings)
 
-        return torch.cat(guidance, dim=-1).flatten(start_dim=1)
+        self.guidance_obs = torch.cat(guidance, dim=-1)
+
+        return self.guidance_obs.flatten(start_dim=1)
 
     def _get_ego_state(self, mask=None) -> torch.Tensor:
         """Get the ego state."""
@@ -1451,7 +1453,7 @@ def get_obs(self, mask=None):
         ego_states = self._get_ego_state(mask)
         partner_observations = self._get_partner_obs(mask)
         road_map_observations = self._get_road_map_obs(mask)
-        guidance = self._get_guidance(mask)
+        guidance_obs = self._get_guidance(mask)
 
         if self.config.use_vbd and self.config.vbd_in_obs:
             # Add ego-centric VBD trajectories
@@ -1472,7 +1474,7 @@ def get_obs(self, mask=None):
                     ego_states,
                     partner_observations,
                     road_map_observations,
-                    guidance,
+                    guidance_obs,
                 ),
                 dim=-1,
             )
diff --git a/gpudrive/visualize/color.py b/gpudrive/visualize/color.py
index 4cf250dec..75db464d5 100644
--- a/gpudrive/visualize/color.py
+++ b/gpudrive/visualize/color.py
@@ -12,6 +12,7 @@
     4: np.array([200, 200, 200]) / 255.0,  # 'Crosswalk' (Light Gray)
     5: np.array([0.85, 0.65, 0.13]),  # 'SpeedBump' (Dark yellow)
     6: np.array([255, 0, 0]) / 255.0,  # 'StopSign' (Red)
+    7: np.array([0.0, 0.0, 0.0]),  # 'RoadEdge' (Black)
 }
 
 ROAD_GRAPH_TYPE_NAMES = {  # 1-6 ; 0 is None (padding)
diff --git a/gpudrive/visualize/core.py b/gpudrive/visualize/core.py
index c51a8677d..318183787 100644
--- a/gpudrive/visualize/core.py
+++ b/gpudrive/visualize/core.py
@@ -22,6 +22,7 @@
     LocalEgoState,
     GlobalEgoState,
     PartnerObs,
+    LidarObs,
 )
 from gpudrive.datatypes.trajectory import LogTrajectory, VBDTrajectory
 from gpudrive.datatypes.control import ResponseType
@@ -53,7 +54,7 @@ def __init__(
         self.goal_radius = goal_radius
         self.num_worlds = num_worlds
         self.render_config = render_config
-        self.figsize = (15, 15)
+        self.figsize = (10, 10)
         self.env_config = env_config
         self.render_3d = render_config.render_3d
         self.vehicle_height = render_config.vehicle_height
@@ -1565,52 +1566,6 @@ def plot_agent_group_2d(bboxes, color, by_policy=False):
                 bboxes_static, AGENT_COLOR_BY_STATE["log_replay"]
             )
 
-    def _plot_expert_trajectories(
-        self,
-        ax: matplotlib.axes.Axes,
-        env_idx: int,
-        expert_trajectories: torch.Tensor,
-        response_type: Any,
-    ) -> None:
-        """Plot expert trajectories.
-        Args:
-            ax: Matplotlib axis for plotting.
-            env_idx: Environment index to select specific environment agents.
-            expert_trajectories: The global state of expert from `LogTrajectory`.
-        """
-        if self.vis_config.draw_expert_trajectories:
-            controlled_mask = self.controlled_agents[env_idx, :]
-            non_controlled_mask = (
-                ~response_type.static[env_idx, :]
-                & response_type.moving[env_idx, :]
-                & ~controlled_mask
-            )
-            mask = (
-                controlled_mask
-                if self.vis_config.draw_only_controllable_veh
-                else controlled_mask | non_controlled_mask
-            )
-            agent_indices = torch.where(mask)[0]
-            trajectories = expert_trajectories[env_idx][mask]
-            for idx, trajectory in zip(agent_indices, trajectories):
-                color = (
-                    AGENT_COLOR_BY_STATE["ok"]
-                    if controlled_mask[idx]
-                    else AGENT_COLOR_BY_STATE["log_replay"]
-                )
-                for step in trajectory:
-                    x, y = step[:2].numpy()
-                    if x < OUT_OF_BOUNDS and y < OUT_OF_BOUNDS:
-                        ax.add_patch(
-                            Circle(
-                                (x, y),
-                                radius=0.3,
-                                color=color,
-                                fill=True,
-                                alpha=0.5,
-                            )
-                        )
-
     def plot_agent_observation(
         self,
         agent_idx: int,
@@ -1628,27 +1583,43 @@ def plot_agent_observation(
                 Should be of shape (N, 2) where N is the number of points and each point
                 is an (x, y) coordinate. Defaults to None.
         """
-        observation_ego = LocalEgoState.from_tensor(
-            self_obs_tensor=self.sim_object.self_observation_tensor(),
-            backend=self.backend,
-            device="cpu",
-        )
 
-        observation_roadgraph = LocalRoadGraphPoints.from_tensor(
-            local_roadgraph_tensor=self.sim_object.agent_roadmap_tensor(),
-            backend=self.backend,
-            device="cpu",
-        )
+        observation_ego = None
+        if self.env_config.ego_state:
+            observation_ego = LocalEgoState.from_tensor(
+                self_obs_tensor=self.sim_object.self_observation_tensor(),
+                backend=self.backend,
+                device="cpu",
+            )
 
-        observation_partner = PartnerObs.from_tensor(
-            partner_obs_tensor=self.sim_object.partner_observations_tensor(),
-            backend=self.backend,
-            device="cpu",
+        observation_roadgraph = None
+        if self.env_config.road_map_obs:
+            observation_roadgraph = LocalRoadGraphPoints.from_tensor(
+                local_roadgraph_tensor=self.sim_object.agent_roadmap_tensor(),
+                backend=self.backend,
+                device="cpu",
+            )
+
+        observation_partner = None
+        if self.env_config.partner_obs:
+            observation_partner = PartnerObs.from_tensor(
+                partner_obs_tensor=self.sim_object.partner_observations_tensor(),
+                backend=self.backend,
+                device="cpu",
+            )
+
+        lidar_obs = (
+            None  # Note: Lidar obs are in global coordinates by default
         )
+        if self.env_config.lidar_obs:
+            lidar_obs = LidarObs.from_tensor(
+                lidar_tensor=self.sim_object.lidar_tensor(),
+                backend=self.backend,
+                device="cpu",
+            )
 
-        # Check if agent index is valid, otherwise return None
-        if observation_ego.id[env_idx, agent_idx] == -1:
-            return None, None
+        marker_scale = max(figsize) / 15
+        line_width_scale = max(figsize) / 15
 
         fig, ax = plt.subplots(figsize=figsize)
         ax.clear()  # Clear any previous plots
@@ -1680,7 +1651,7 @@ def plot_agent_observation(
                     x_points,
                     y_points,
                     c=[ROAD_GRAPH_COLORS[road_type]],
-                    s=8,
+                    s=8 * marker_scale,
                     label=type_name,
                 )
 
@@ -1707,28 +1678,28 @@ def plot_agent_observation(
                         [y_start - width_dy, y_end - width_dy],
                         color=ROAD_GRAPH_COLORS[road_type],
                         alpha=0.5,
-                        linewidth=1.0,
+                        linewidth=line_width_scale,
                     )
                     ax.plot(
                         [x_start + width_dx, x_end + width_dx],
                         [y_start + width_dy, y_end + width_dy],
                         color=ROAD_GRAPH_COLORS[road_type],
                         alpha=0.5,
-                        linewidth=1.0,
+                        linewidth=line_width_scale,
                     )
                     ax.plot(
                         [x_start - width_dx, x_start + width_dx],
                         [y_start - width_dy, y_start + width_dy],
                         color=ROAD_GRAPH_COLORS[road_type],
                         alpha=0.5,
-                        linewidth=1.0,
+                        linewidth=line_width_scale,
                     )
                     ax.plot(
                         [x_end - width_dx, x_end + width_dx],
                         [y_end - width_dy, y_end + width_dy],
                         color=ROAD_GRAPH_COLORS[road_type],
                         alpha=0.5,
-                        linewidth=1.0,
+                        linewidth=line_width_scale,
                     )
 
         # Plot partner agents if provided
@@ -1759,9 +1730,14 @@ def plot_agent_observation(
                 ].squeeze(),
                 color=REL_OBS_OBJ_COLORS["other_agents"],
                 alpha=1.0,
+                line_width_scale=line_width_scale,
             )
 
         if observation_ego is not None:
+            # Check if agent index is valid, otherwise return None
+            if observation_ego.id[env_idx, agent_idx] == -1:
+                return None, None
+
             ego_agent_color = (
                 "r"
                 if observation_ego.is_collided[env_idx, agent_idx]
@@ -1796,37 +1772,32 @@ def plot_agent_observation(
                 zorder=1,
             )
 
-            # ax.scatter(
-            #     observation_ego.rel_goal_x[env_idx, agent_idx],
-            #     observation_ego.rel_goal_y[env_idx, agent_idx],
-            #     s=5,
-            #     linewidth=1.5,
-            #     c=ego_agent_color,
-            #     marker="x",
-            # )
-
-            # circle = Circle(
-            #     (
-            #         observation_ego.rel_goal_x[env_idx, agent_idx],
-            #         observation_ego.rel_goal_y[env_idx, agent_idx],
-            #     ),
-            #     radius=self.goal_radius,
-            #     color=ego_agent_color,
-            #     fill=False,
-            #     linestyle="--",
-            # )
-            # ax.add_patch(circle)
-
-            # observation_radius = Circle(
-            #     (0, 0),
-            #     radius=self.env_config.obs_radius,
-            #     color="#000000",
-            #     linewidth=0.8,
-            #     fill=False,
-            #     linestyle="-",
-            # )
-            # ax.add_patch(observation_radius)
-            plt.axis("off")
+        if lidar_obs is not None:
+            num_lidar_samples = lidar_obs.num_lidar_samples * 3
+
+            ego_lidar_pos_xy = (
+                lidar_obs.all_lidar_samples[env_idx, agent_idx, :, :, 2:4]
+                .flatten(end_dim=1)
+                .cpu()
+                .numpy()
+            )
+
+            ego_lidar_entity_types = (
+                lidar_obs.all_lidar_samples[env_idx, agent_idx, :, :, 1]
+                .flatten()
+                .cpu()
+                .numpy()
+            )
+
+            for lidar_sample_idx in range(num_lidar_samples):
+                ax.scatter(
+                    ego_lidar_pos_xy[lidar_sample_idx, 0],
+                    ego_lidar_pos_xy[lidar_sample_idx, 1],
+                    s=2,
+                    marker="o",
+                    c="k",
+                    alpha=0.5,
+                )
 
         time_step = (
             self.env_config.episode_len
@@ -1847,17 +1818,16 @@ def plot_agent_observation(
             va="top",
         )
 
-        attn_reference_idx = torch.where(trajectory[:, 2] == 1)[0]
-
         if trajectory is not None and len(trajectory) > 0:
+            attn_reference_idx = torch.where(trajectory[:, 2] == 1)[0]
             # Plot the trajectory as a line
             ax.scatter(
                 trajectory[:, 0],  # x coordinates
                 trajectory[:, 1],  # y coordinates
                 color="g",
-                linewidth=0.3,
+                linewidth=0.2 * line_width_scale,
                 marker="o",
-                alpha=0.2,
+                alpha=0.1,
                 zorder=0,
             )
             # Add a purple star above the reference position
@@ -1879,5 +1849,6 @@ def plot_agent_observation(
         ax.set_ylim((-self.env_config.obs_radius, self.env_config.obs_radius))
         ax.set_xticks([])
         ax.set_yticks([])
+        plt.axis("off")
 
         return fig
diff --git a/gpudrive/visualize/utils.py b/gpudrive/visualize/utils.py
index 094c1d030..5fa4a8566 100644
--- a/gpudrive/visualize/utils.py
+++ b/gpudrive/visualize/utils.py
@@ -14,25 +14,26 @@
 
 from gpudrive.visualize.color import ROAD_GRAPH_COLORS, ROAD_GRAPH_TYPE_NAMES
 
+
 def img_from_fig(fig: matplotlib.figure.Figure) -> np.ndarray:
     """Returns a [H, W, 3] uint8 np image from fig.canvas.tostring_rgb()."""
     # Adjusted margins to better accommodate 3D plots
     fig.subplots_adjust(
-        left=0.0,    # Reduce left margin
+        left=0.0,  # Reduce left margin
         bottom=0.0,  # Reduce bottom margin
-        right=1.0,   # Extend to right edge
-        top=1.0,     # Extend to top edge
-        wspace=0.0, 
-        hspace=0.0
+        right=1.0,  # Extend to right edge
+        top=1.0,  # Extend to top edge
+        wspace=0.0,
+        hspace=0.0,
     )
-    
+
     # Force render
     fig.canvas.draw()
-    
+
     # Convert to numpy array
     data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
     img = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    
+
     plt.close(fig)
     return img
 
@@ -158,6 +159,7 @@ def plot_bounding_box(
     orientation: Union[float, torch.Tensor],
     color: str,
     alpha: Optional[float] = 1.0,
+    line_width_scale: float = 1.5,
     label: Optional[str] = None,
 ) -> None:
     """Plots bounding boxes, supporting both single and multiple agents.
@@ -223,7 +225,7 @@ def plot_bounding_box(
                 color=color,
                 alpha=alpha,
                 linestyle="-",
-                linewidth=2,
+                linewidth=2 * line_width_scale,
                 label=label if i == 0 else None,
             )
     else:
@@ -456,7 +458,7 @@ def plot_numpy_bounding_boxes_multiple_policy(
       label: String, represents the meaning of the color for different boxes.
     """
 
-    for bboxes,color in zip(bboxes_s,colors):
+    for bboxes, color in zip(bboxes_s, colors):
         if bboxes.ndim != 2 or bboxes.shape[1] != 5:
             raise ValueError(
                 (
@@ -515,4 +517,4 @@ def plot_numpy_bounding_boxes_multiple_policy(
                 alpha=alpha,
                 linewidth=1.5 * line_width_scale,
                 label=label,
-            )
\ No newline at end of file
+            )

From 47713fff4fb313edbd1abc6e52230a5a9241ff30 Mon Sep 17 00:00:00 2001
From: Daphne Cornelisse <cor.daphne@gmail.com>
Date: Thu, 1 May 2025 16:09:12 -0400
Subject: [PATCH 04/38] Hot model fix

---
 ...eplay__S_100__04_29_19_49_30_053_015179.pt | Bin 0 -> 1493922 bytes
 ..._replay__S_1__04_26_09_02_20_677_000833.pt | Bin 1353210 -> 0 bytes
 ..._replay__S_3__04_27_13_13_33_780_013762.pt | Bin 1295482 -> 0 bytes
 examples/eval/run_wosac_eval.py               |  12 +++++-------
 gpudrive/networks/agents.py                   |   6 +++---
 5 files changed, 8 insertions(+), 10 deletions(-)
 create mode 100644 checkpoints/model_guidance_log_replay__S_100__04_29_19_49_30_053_015179.pt
 delete mode 100644 checkpoints/model_guidance_log_replay__S_1__04_26_09_02_20_677_000833.pt
 delete mode 100644 checkpoints/model_guidance_log_replay__S_3__04_27_13_13_33_780_013762.pt

diff --git a/checkpoints/model_guidance_log_replay__S_100__04_29_19_49_30_053_015179.pt b/checkpoints/model_guidance_log_replay__S_100__04_29_19_49_30_053_015179.pt
new file mode 100644
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GIT binary patch
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diff --git a/checkpoints/model_guidance_log_replay__S_1__04_26_09_02_20_677_000833.pt b/checkpoints/model_guidance_log_replay__S_1__04_26_09_02_20_677_000833.pt
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QeN2F~TK(bu{FcxCA2gQ>0ssI2

diff --git a/examples/eval/run_wosac_eval.py b/examples/eval/run_wosac_eval.py
index 6e5178737..15101b32b 100644
--- a/examples/eval/run_wosac_eval.py
+++ b/examples/eval/run_wosac_eval.py
@@ -228,16 +228,16 @@ def rollout(
 
     # Settings
     MAX_AGENTS = 64
-    NUM_ENVS = 3
+    NUM_ENVS = 1
     DEVICE = "cuda"  # where to run the env rollouts
     NUM_ROLLOUTS_PER_BATCH = 1
     NUM_DATA_BATCHES = 1
     INIT_STEPS = 10
     DATASET_SIZE = 100
-    RENDER = True
+    RENDER = False
 
-    DATA_JSON = "data/processed/wosac/validation_json_3"
-    DATA_TFRECORD = "data/processed/wosac/validation_tfrecord_3"
+    DATA_JSON = "data/processed/wosac/validation_json_100"
+    DATA_TFRECORD = "data/processed/wosac/validation_tfrecord_100"
 
     # Create data loader
     val_loader = SceneDataLoader(
@@ -251,9 +251,7 @@ def rollout(
 
     # Load agent
     agent = load_agent(
-        # path_to_cpt="checkpoints/model_guidance_log_replay__S_1__04_26_09_02_20_677_000833.pt",
-        # path_to_cpt="checkpoints/model_guidance_log_replay__S_3__04_27_13_13_33_780_013762.pt", # Trained with collision penalty
-        path_to_cpt="checkpoints/model_guidance_log_replay__S_3__04_28_15_56_44_152_014083.pt",  # Trained without collision penalty
+        path_to_cpt="checkpoints/model_guidance_log_replay__S_100__04_29_19_49_30_053_015179.pt", 
     ).to(DEVICE)
 
     # Override default environment settings to match those the agent was trained with
diff --git a/gpudrive/networks/agents.py b/gpudrive/networks/agents.py
index 565374a35..a6e16e295 100644
--- a/gpudrive/networks/agents.py
+++ b/gpudrive/networks/agents.py
@@ -107,10 +107,10 @@ def __init__(
 
         # Critic network
         self.critic = nn.Sequential(
-            layer_init(nn.Linear((2 * top_k + 2) * embed_dim, 32)),
-            nn.LayerNorm(32),
+            layer_init(nn.Linear((2 * top_k + 2) * embed_dim, 64)),
+            nn.LayerNorm(64),
             self.act_func,
-            layer_init(nn.Linear(32, 1), std=1.0),
+            layer_init(nn.Linear(64, 1), std=1.0),
         )
 
         # Actor network

From e7f3bcba37abbbd40da7ecf4c7db42517c6f230c Mon Sep 17 00:00:00 2001
From: Daphne Cornelisse <33460159+daphne-cornelisse@users.noreply.github.com>
Date: Fri, 2 May 2025 10:12:59 -0400
Subject: [PATCH 05/38] Support for online guidance with `vbd_online` (#431)

* Delete obsolete vbd functions

* Format

* Remove all instances of use_vbd and alike -> unified guidance mode

* Create dataclass struct for VBD online predictionsions

* Delete world_time_steps as it is no longer used

* Leaving a todo

* Make sure to demean the VBD predicted trajectories

* Data analysis and minor changes

* Guidance data analysis notebook

* Script to process guidance data

* Fixes

* Fixes

* Update nb

* Fixes and make sure to always wrap the yaws.

* Fixes and make sure to always wrap the yaws.

* Example data to work with
---
 .gitignore                                    |     2 +-
 baselines/ppo/config/ppo_base_puffer.yaml     |     4 -
 baselines/ppo/config/ppo_guided_autonomy.yaml |     4 +-
 baselines/ppo/config/ppo_population.yaml      |     5 -
 baselines/ppo/ppo_guided_autonomy.py          |     6 -
 baselines/ppo/ppo_pufferlib.py                |     6 -
 .../figures_data/reference_log_replay.npy     |   Bin 0 -> 2577248 bytes
 .../figures_data/reference_vbd_amortized.npy  |   Bin 0 -> 2548928 bytes
 .../figures_data/reference_vbd_online.npy     |   Bin 0 -> 2265728 bytes
 .../notebooks/guidance_data_analysis.ipynb    | 47412 ++++++++++++++++
 .../eval/notebooks/obtain_guidance_data.py    |    63 +
 .../config/reliable_agents_params.yaml        |     1 -
 gpudrive/datatypes/trajectory.py              |    96 +-
 gpudrive/env/config.py                        |    18 +-
 gpudrive/env/constants.py                     |     2 +-
 gpudrive/env/env_puffer.py                    |     8 -
 gpudrive/env/env_torch.py                     |   411 +-
 gpudrive/integrations/puffer/ppo.py           |     2 -
 gpudrive/integrations/vbd/data/amortize.py    |   195 +-
 gpudrive/integrations/vbd/data/utils.py       |    22 +-
 .../notebooks/03_vbd_output_comparison.ipynb  |    46 +-
 gpudrive/networks/late_fusion.py              |    13 -
 22 files changed, 47815 insertions(+), 501 deletions(-)
 create mode 100644 examples/eval/figures_data/reference_log_replay.npy
 create mode 100644 examples/eval/figures_data/reference_vbd_amortized.npy
 create mode 100644 examples/eval/figures_data/reference_vbd_online.npy
 create mode 100644 examples/eval/notebooks/guidance_data_analysis.ipynb
 create mode 100644 examples/eval/notebooks/obtain_guidance_data.py

diff --git a/.gitignore b/.gitignore
index a6084f713..0c70e5cd2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -29,7 +29,7 @@ data/processed/training/*
 data/processed/testing/*
 data/processed/pop_play/*
 data/processed/hand_designed/*
-analyze/figures/*
+figures/
 data/other/*
 wosac/
 
diff --git a/baselines/ppo/config/ppo_base_puffer.yaml b/baselines/ppo/config/ppo_base_puffer.yaml
index b7d633955..8a83715cb 100644
--- a/baselines/ppo/config/ppo_base_puffer.yaml
+++ b/baselines/ppo/config/ppo_base_puffer.yaml
@@ -42,11 +42,7 @@ environment: # Overrides default environment configs (see pygpudrive/env/config.
   obs_radius: 50.0 # Visibility radius of the agents
   action_space_steer_disc: 13
   action_space_accel_disc: 7
-  # Versatile Behavior Diffusion (VBD): This will slow down training
-  use_vbd: false
   init_steps: 0
-  vbd_trajectory_weight: 0.1 # Importance of distance to the vbd trajectories in the reward function
-  vbd_in_obs: false
 
 wandb:
   entity: ""
diff --git a/baselines/ppo/config/ppo_guided_autonomy.yaml b/baselines/ppo/config/ppo_guided_autonomy.yaml
index 458058111..4ebe7f681 100644
--- a/baselines/ppo/config/ppo_guided_autonomy.yaml
+++ b/baselines/ppo/config/ppo_guided_autonomy.yaml
@@ -75,7 +75,7 @@ train:
 
   # # # PPO # # #
   torch_deterministic: false
-  total_timesteps: 2_000_000_000
+  total_timesteps: 4_000_000_000
   batch_size: 131072
   minibatch_size: 8192
   learning_rate: 3e-4
@@ -105,7 +105,7 @@ train:
     num_parameters: 0 # Total trainable parameters, to be filled at runtime
 
   # # # Checkpointing # # #
-  checkpoint_interval: 50 # Save policy every k iterations
+  checkpoint_interval: 500 # Save policy every k iterations
   checkpoint_path: "./runs"
 
   # # # Rendering # # #
diff --git a/baselines/ppo/config/ppo_population.yaml b/baselines/ppo/config/ppo_population.yaml
index 5d02aa13c..06378f9a5 100644
--- a/baselines/ppo/config/ppo_population.yaml
+++ b/baselines/ppo/config/ppo_population.yaml
@@ -42,11 +42,6 @@ environment: # Overrides default environment configs (see pygpudrive/env/config.
   action_space_steer_disc: 13
   action_space_accel_disc: 7
   init_steps: 0 # Warmup steps
-  # Versatile Behavior Diffusion (VBD): This will slow down training
-  use_vbd: false
-  vbd_model_path: "gpudrive/integrations/vbd/weights/epoch=18.ckpt"
-  vbd_trajectory_weight: 0.1 # Importance of distance to the vbd trajectories in the reward function
-  vbd_in_obs: false
 
 wandb:
   entity: ""
diff --git a/baselines/ppo/ppo_guided_autonomy.py b/baselines/ppo/ppo_guided_autonomy.py
index 8f6c2d8c9..6d3ed5b3d 100644
--- a/baselines/ppo/ppo_guided_autonomy.py
+++ b/baselines/ppo/ppo_guided_autonomy.py
@@ -125,10 +125,7 @@ def run(
     obs_radius: Annotated[Optional[float], typer.Option(help="The radius for the observation")] = None,
     collision_behavior: Annotated[Optional[str], typer.Option(help="The collision behavior; 'ignore' or 'remove'")] = None,
     remove_non_vehicles: Annotated[Optional[int], typer.Option(help="Remove non-vehicles from the scene; 0 or 1")] = None,
-    use_vbd: Annotated[Optional[bool], typer.Option(help="Use VBD model for trajectory predictions")] = False,
     vbd_model_path: Annotated[Optional[str], typer.Option(help="Path to VBD model checkpoint")] = None,
-    vbd_trajectory_weight: Annotated[Optional[float], typer.Option(help="Weight for VBD trajectory deviation penalty")] = 0.1,
-    vbd_in_obs: Annotated[Optional[bool], typer.Option(help="Include VBD predictions in the observation")] = False,
     init_steps: Annotated[Optional[int], typer.Option(help="Environment warmup steps")] = 0,
 
     # Train options
@@ -185,10 +182,7 @@ def run(
         "remove_non_vehicles": None
         if remove_non_vehicles is None
         else bool(remove_non_vehicles),
-        "use_vbd": use_vbd,
         "vbd_model_path": vbd_model_path,
-        "vbd_trajectory_weight": vbd_trajectory_weight,
-        "vbd_in_obs": vbd_in_obs,
         "init_steps": init_steps,
     }
     config.environment.update(
diff --git a/baselines/ppo/ppo_pufferlib.py b/baselines/ppo/ppo_pufferlib.py
index b92467459..142222d1e 100644
--- a/baselines/ppo/ppo_pufferlib.py
+++ b/baselines/ppo/ppo_pufferlib.py
@@ -172,9 +172,6 @@ def run(
     obs_radius: Annotated[Optional[float], typer.Option(help="The radius for the observation")] = None,
     collision_behavior: Annotated[Optional[str], typer.Option(help="The collision behavior; 'ignore' or 'remove'")] = None,
     remove_non_vehicles: Annotated[Optional[int], typer.Option(help="Remove non-vehicles from the scene; 0 or 1")] = None,
-    use_vbd: Annotated[Optional[bool], typer.Option(help="Use VBD model for trajectory predictions")] = False,
-    vbd_trajectory_weight: Annotated[Optional[float], typer.Option(help="Weight for VBD trajectory deviation penalty")] = 0.1,
-    vbd_in_obs: Annotated[Optional[bool], typer.Option(help="Include VBD predictions in the observation")] = False,
     init_steps: Annotated[Optional[int], typer.Option(help="Environment warmup steps")] = 0,
     # Train options
     seed: Annotated[Optional[int], typer.Option(help="The seed for training")] = None,
@@ -227,9 +224,6 @@ def run(
         "remove_non_vehicles": None
         if remove_non_vehicles is None
         else bool(remove_non_vehicles),
-        "use_vbd": use_vbd,
-        "vbd_trajectory_weight": vbd_trajectory_weight,
-        "vbd_in_obs": vbd_in_obs,
         "init_steps": init_steps,
     }
     config.environment.update(
diff --git a/examples/eval/figures_data/reference_log_replay.npy b/examples/eval/figures_data/reference_log_replay.npy
new file mode 100644
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diff --git a/examples/eval/figures_data/reference_vbd_amortized.npy b/examples/eval/figures_data/reference_vbd_amortized.npy
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diff --git a/examples/eval/figures_data/reference_vbd_online.npy b/examples/eval/figures_data/reference_vbd_online.npy
new file mode 100644
index 0000000000000000000000000000000000000000..605d774edae7751efa51f5007985c0c06092d725
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diff --git a/examples/eval/notebooks/guidance_data_analysis.ipynb b/examples/eval/notebooks/guidance_data_analysis.ipynb
new file mode 100644
index 000000000..708b09109
--- /dev/null
+++ b/examples/eval/notebooks/guidance_data_analysis.ipynb
@@ -0,0 +1,47412 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import importlib\n",
+    "import gpudrive\n",
+    "importlib.reload(gpudrive)\n",
+    "\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import torch\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# Set working directory to the base directory 'gpudrive_madrona'\n",
+    "working_dir = Path.cwd()\n",
+    "while working_dir.name != 'gpudrive':\n",
+    "    working_dir = working_dir.parent\n",
+    "    if working_dir == Path.home():\n",
+    "        raise FileNotFoundError(\"Base directory 'gpudrive_madrona' not found\")\n",
+    "os.chdir(working_dir)\n",
+    "import torch\n",
+    "from PIL import Image\n",
+    "import seaborn as sns\n",
+    "import warnings\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import datetime\n",
+    "\n",
+    "from gpudrive.env.env_torch import GPUDriveTorchEnv\n",
+    "from gpudrive.env.config import EnvConfig, RenderConfig\n",
+    "from gpudrive.env.dataset import SceneDataLoader\n",
+    "from gpudrive.visualize.utils import img_from_fig\n",
+    "\n",
+    "sns.set(\"notebook\", font_scale=1.05, rc={\"figure.figsize\": (10, 5)})\n",
+    "sns.set_style(\"ticks\", rc={\"figure.facecolor\": \"none\", \"axes.facecolor\": \"none\"})\n",
+    "%config InlineBackend.figure_format = 'svg'\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "plt.set_loglevel(\"WARNING\")\n",
+    "mpl.rcParams[\"lines.markersize\"] = 8\n",
+    "\n",
+    "plt.set_loglevel(\"WARNING\")\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Settings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATASET = \"data/processed/wosac/validation_json_100\"\n",
+    "FIGURES_DIR = \"examples/eval/figures\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['data/processed/wosac/validation_json_100/12858b7f79387840.json', 'data/processed/wosac/validation_json_100/14929c51c7298df1.json']\n"
+     ]
+    }
+   ],
+   "source": [
+    "GUIDANCE_MODE = \"vbd_amortized\"\n",
+    "\n",
+    "env_config = EnvConfig(\n",
+    "    dynamics_model=\"classic\",\n",
+    "    reward_type=\"guided_autonomy\",\n",
+    "    guidance=True,\n",
+    "    guidance_mode=GUIDANCE_MODE,\n",
+    "    add_reference_heading=True,\n",
+    "    add_reference_speed=True,\n",
+    "    add_reference_pos_xy=True,\n",
+    "    init_mode=\"wosac_train\",\n",
+    ")\n",
+    "render_config = RenderConfig()\n",
+    "\n",
+    "train_loader = SceneDataLoader(\n",
+    "    root=DATASET,\n",
+    "    batch_size=2,\n",
+    "    dataset_size=100,\n",
+    "    sample_with_replacement=False,\n",
+    "    shuffle=False,\n",
+    "    file_prefix=\"\",\n",
+    ")\n",
+    "\n",
+    "env = GPUDriveTorchEnv(\n",
+    "    config=env_config,\n",
+    "    data_loader=train_loader,\n",
+    "    max_cont_agents=64, \n",
+    "    device=\"cpu\",\n",
+    ")\n",
+    "\n",
+    "print(env.data_batch)\n",
+    "\n",
+    "obs = env.reset(env.cont_agent_mask)\n",
+    "\n",
+    "# # Save for analysis\n",
+    "# reference_traj = torch.cat([\n",
+    "#     env.reference_trajectory.pos_xy,\n",
+    "#     env.reference_trajectory.vel_xy,\n",
+    "#     env.reference_trajectory.yaw,\n",
+    "#     env.reference_trajectory.valids,\n",
+    "# ], dim=-1)\n",
+    "# reference_traj_np = reference_traj[env.cont_agent_mask].numpy()\n",
+    "\n",
+    "# np.save(f\"reference_{GUIDANCE_MODE}.npy\", reference_traj_np)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Q1. Are the distributions from different modes (`vbd_online`, `log_replay` and `vbd_amortized`) on the same scale?\n",
+    "\n",
+    "- Unnormalized values\n",
+    "- Macroscopic (full dist)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load npy array from the file\n",
+    "FOLDER = \"examples/eval/figures_data/\"\n",
+    "\n",
+    "ref_log_replay = np.load(f\"{FOLDER}reference_log_replay.npy\")\n",
+    "ref_vbd_amortized = np.load(f\"{FOLDER}reference_vbd_amortized.npy\")\n",
+    "ref_vbd_online = np.load(f\"{FOLDER}reference_vbd_online.npy\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((1180, 91, 6), (1180, 90, 6), (1180, 80, 6))"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Controlled agent elements for 10 scenarios\n",
+    "ref_log_replay.shape, ref_vbd_amortized.shape, ref_vbd_online.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Q1 | **Global positions** $(x,y)$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
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+       "   <g id=\"text_36\">\n",
+       "    <!-- vbd_online -->\n",
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+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 1300x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(13, 3),)# sharex=True, sharey=True)\n",
+    "\n",
+    "axs[0].hist(ref_log_replay[:, :, :2].flatten(), color='b', alpha=0.5, bins=20, label=\"log_replay\")\n",
+    "axs[1].hist(ref_vbd_amortized[:, :, :2].flatten(), color='r', alpha=1.0, bins=20, label=\"vbd_amortized\")\n",
+    "axs[2].hist(ref_vbd_online[:, :, :2].flatten(), color='g', alpha=1.0, bins=20, label=\"vbd_online\")\n",
+    "\n",
+    "axs[0].set_xlabel(\"Demeaned position in global coord. frame\")\n",
+    "axs[0].set_ylabel(\"Count\")\n",
+    "\n",
+    "fig.legend()\n",
+    "sns.despine()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Q1 | **Velocities** $(x,y)$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
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+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 1300x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(13, 3) )# sharex=True, sharey=True)\n",
+    "\n",
+    "axs[0].hist(ref_log_replay[:, :, 2:4].flatten(), color='b', alpha=0.5, bins=20, label=\"log_replay\")\n",
+    "axs[1].hist(ref_vbd_amortized[:, :, 2:4].flatten(), color='r', alpha=1.0, bins=20, label=\"vbd_amortized\")\n",
+    "axs[2].hist(ref_vbd_online[:, :, 2:4].flatten(), color='g', alpha=1.0, bins=20, label=\"vbd_online\")\n",
+    "\n",
+    "axs[0].set_xlabel(\"Demeaned velocity [m/s]\")\n",
+    "axs[0].set_ylabel(\"Count\")\n",
+    "\n",
+    "fig.legend()\n",
+    "sns.despine()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Q1 | **Headings**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
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+      ],
+      "text/plain": [
+       "<Figure size 1300x300 with 3 Axes>"
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+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(13, 3),)# sharex=True, sharey=True)\n",
+    "\n",
+    "axs[0].hist(ref_log_replay[:, :, 4].flatten(), color='b', alpha=0.5, bins=20, label=\"log_replay\")\n",
+    "axs[1].hist(ref_vbd_amortized[:, :, 4].flatten(), color='r', alpha=1.0, bins=20, label=\"vbd_amortized\")\n",
+    "axs[2].hist(ref_vbd_online[:, :, 4].flatten(), color='g', alpha=1.0, bins=20, label=\"vbd_online\")\n",
+    "\n",
+    "axs[0].set_xlabel(\"Headings in global coord. frame [rad]\")\n",
+    "axs[0].set_ylabel(\"Count\")\n",
+    "\n",
+    "# add legend\n",
+    "fig.legend()\n",
+    "sns.despine()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Q2. How do the trajectories differ qualitatively?\n",
+    "\n",
+    "- Plot several guidance trajectories for a particular agent to ensure that they are on the same scale"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
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+       "    <!-- Global heading; agent 9 -->\n",
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+       "   <g id=\"text_40\">\n",
+       "    <!-- vbd_online -->\n",
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+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 1200x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for agent_idx in range(10):\n",
+    "    \n",
+    "    valid = ref_log_replay[agent_idx, :, -1].astype( bool)\n",
+    "\n",
+    "    fig, axs = plt.subplots(1, 3, figsize=(12, 4))\n",
+    "    axs[0].set_title(f\"Global position (x, y); agent {agent_idx}\")\n",
+    "    axs[0].plot(ref_log_replay[agent_idx, :, 0][valid], ref_log_replay[agent_idx, :, 1][valid], color='b')\n",
+    "    #axs[0].plot(ref_vbd_online[agent_idx, :, 0], ref_vbd_online[agent_idx, :, 1], color='g', alpha=0.5)\n",
+    "    axs[0].plot(ref_vbd_amortized[agent_idx, :, 0], ref_vbd_amortized[agent_idx, :, 1], color='r')\n",
+    "    axs[0].set_xlabel(\"x\")\n",
+    "    axs[0].set_ylabel(\"y\")\n",
+    "\n",
+    "    axs[1].set_title(f\"Global velocity (x, y); agent {agent_idx}\")\n",
+    "    axs[1].plot(ref_log_replay[agent_idx, :, 2][valid], ref_log_replay[agent_idx, :, 3][valid], color='b')\n",
+    "    axs[1].plot(ref_vbd_online[agent_idx, :, 2], ref_vbd_online[agent_idx, :, 3], color='g')\n",
+    "    axs[1].plot(ref_vbd_amortized[agent_idx, :, 2], ref_vbd_amortized[agent_idx, :, 3], color='r')\n",
+    "    axs[2].set_xlabel(r\"$v_x$\")\n",
+    "    axs[2].set_ylabel(r\"$v_y$\")\n",
+    "\n",
+    "    axs[2].set_title(f\"Global heading; agent {agent_idx}\")\n",
+    "    axs[2].plot(ref_log_replay[agent_idx, :, 4][valid], color='b', label=\"log_replay\")\n",
+    "    axs[2].plot(ref_vbd_online[agent_idx, :, 4], color='g', label=\"vbd_online\")\n",
+    "    axs[2].plot(ref_vbd_amortized[agent_idx, :, 4], color='r', label=\"vbd_amortized\")\n",
+    "    axs[2].set_ylabel(r\"$\\theta$\")\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "\n",
+    "    fig.legend(loc=\"center right\", facecolor=\"white\", bbox_to_anchor=(1.12, 0.5))\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Show corresponding full agent views"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+   "source": [
+    "center_agent_idx=3\n",
+    "\n",
+    "traj_masked = env.reference_path[center_agent_idx, :, :]\n",
+    "\n",
+    "agent_obs_masked = env.vis.plot_agent_observation(\n",
+    "    env_idx=0,\n",
+    "    agent_idx=center_agent_idx,\n",
+    "    figsize=(8, 8),\n",
+    "    trajectory=traj_masked,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
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+    "center_agent_idx=1\n",
+    "\n",
+    "traj_masked = env.reference_path[center_agent_idx, :, :]\n",
+    "\n",
+    "agent_obs_masked = env.vis.plot_agent_observation(\n",
+    "    env_idx=0,\n",
+    "    agent_idx=center_agent_idx,\n",
+    "    figsize=(8, 8),\n",
+    "    trajectory=traj_masked,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
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+      ],
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "center_agent_idx=0\n",
+    "\n",
+    "traj_masked = env.reference_path[center_agent_idx, :, :]\n",
+    "\n",
+    "agent_obs_masked = env.vis.plot_agent_observation(\n",
+    "    env_idx=0,\n",
+    "    agent_idx=center_agent_idx,\n",
+    "    figsize=(8, 8),\n",
+    "    trajectory=traj_masked,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simulator state for that scene"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
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+   "source": [
+    "sim_states = env.vis.plot_simulator_state(\n",
+    "    env_indices=0,\n",
+    "    zoom_radius=70,\n",
+    "    plot_guidance_pos_xy=True,\n",
+    "    center_agent_indices=[center_agent_idx],\n",
+    ")\n",
+    "sim_states[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Useful dataset statistics\n",
+    "\n",
+    "- What is the distribution of the guidance trajectories in the dataset?\n",
+    "    - What is the average length $T$?, what is the variance?\n",
+    "- How often is the guidance trajectory to \"do nothing\"?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1180, 91, 6)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ref_log_replay.shape # [agents, time, features]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "valid_guidance_points = ref_log_replay[:, :, 5].sum(axis=-1)\n",
+    "valid_guidance_points.shape\n",
+    "\n",
+    "# Some trajectories, while valid, are just parked cars\n",
+    "# Detect these by checking for zero velocity trajectories\n",
+    "valid_and_non_zero = ((ref_log_replay[:, :, 2] != 0) & (ref_log_replay[:, :, 5] == 1)).sum(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
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+      "text/plain": [
+       "<Figure size 900x350 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 2, figsize=(9, 3.5), sharey=True)\n",
+    "\n",
+    "fig.suptitle(f\"Distribution of guidance points per episode; A = {ref_log_replay.shape[0]} (50 scenarios)\")\n",
+    "\n",
+    "sns.histplot(valid_guidance_points, bins=10, stat='percent', ax=axs[0])\n",
+    "axs[0].grid(True, alpha=0.2)\n",
+    "axs[0].set_xlabel(\"Guidance points per episode.\")\n",
+    "mean_value = valid_guidance_points.mean()\n",
+    "axs[0].axvline(x=mean_value, color='purple', linestyle='--')\n",
+    "axs[0].text(mean_value-3, axs[0].get_ylim()[1]*0.95, r'$\\mu$', color='purple', \n",
+    "            fontsize=14, ha='center', va='center')\n",
+    "\n",
+    "sns.histplot(valid_and_non_zero, bins=10, stat='percent', ax=axs[1])\n",
+    "axs[1].grid(True, alpha=0.2)\n",
+    "axs[1].set_xlabel(r\"$\\bf{Nonzero}$ guidance points per episode.\")\n",
+    "mean_value_nonzero = valid_and_non_zero.mean()\n",
+    "axs[1].axvline(x=mean_value_nonzero, color='purple', linestyle='--')\n",
+    "axs[1].text(mean_value_nonzero-3, axs[1].get_ylim()[1]*0.95, r'$\\mu$', color='purple', fontsize=14, ha='center', va='center')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "sns.despine()\n",
+    "plt.savefig(f\"{FIGURES_DIR}/guidance_points_distribution.pdf\", dpi=300, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(69.444916, 30.798435, 91.0, 1.0)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "valid_guidance_points.mean(), valid_guidance_points.std(), valid_guidance_points.max(), valid_guidance_points.min()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "gpudrive",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/eval/notebooks/obtain_guidance_data.py b/examples/eval/notebooks/obtain_guidance_data.py
new file mode 100644
index 000000000..1f67b0166
--- /dev/null
+++ b/examples/eval/notebooks/obtain_guidance_data.py
@@ -0,0 +1,63 @@
+import importlib
+import gpudrive
+
+importlib.reload(gpudrive)
+from gpudrive.env.env_torch import GPUDriveTorchEnv
+from gpudrive.env.config import EnvConfig, RenderConfig
+from gpudrive.env.dataset import SceneDataLoader
+import torch
+import numpy as np
+
+
+if __name__ == "__main__":
+
+    GUIDANCE_MODE = "vbd_online"
+    DATASET = "data/processed/wosac/validation_json_100"  # Ensure VBD trajectory structures are in here
+    SAVE_PATH = "examples/eval/figures_data/"
+
+    env_config = EnvConfig(
+        dynamics_model="classic",
+        reward_type="guided_autonomy",
+        guidance=True,
+        guidance_mode=GUIDANCE_MODE,
+        add_reference_heading=True,
+        add_reference_speed=True,
+        add_reference_pos_xy=True,
+        init_mode="wosac_train",
+    )
+    render_config = RenderConfig()
+
+    train_loader = SceneDataLoader(
+        root=DATASET,
+        batch_size=50,
+        dataset_size=100,
+        sample_with_replacement=False,
+        shuffle=False,
+        file_prefix="",
+    )
+
+    env = GPUDriveTorchEnv(
+        config=env_config,
+        data_loader=train_loader,
+        max_cont_agents=64,
+        device="cpu",
+    )
+
+    obs = env.reset(env.cont_agent_mask)
+
+    # Save for analysis
+    reference_traj = torch.cat(
+        [
+            env.reference_trajectory.pos_xy,
+            env.reference_trajectory.vel_xy,
+            env.reference_trajectory.yaw,
+            env.reference_trajectory.valids,
+        ],
+        dim=-1,
+    )
+    reference_traj_np = (
+        reference_traj[env.cont_agent_mask].detach().cpu().numpy()
+    )
+
+    np.save(f"{SAVE_PATH}reference_{GUIDANCE_MODE}.npy", reference_traj_np)
+    print(f"Saved reference trajectory for {GUIDANCE_MODE} mode.")
diff --git a/examples/experimental/config/reliable_agents_params.yaml b/examples/experimental/config/reliable_agents_params.yaml
index 004e8e504..afb7404f5 100644
--- a/examples/experimental/config/reliable_agents_params.yaml
+++ b/examples/experimental/config/reliable_agents_params.yaml
@@ -19,4 +19,3 @@ obs_radius: 50.0 # Visibility radius of the agents
 action_space_steer_disc: 13
 action_space_accel_disc: 7
 init_mode: all_non_trivial
-vbd_in_obs: false # If true, the VBD is included in the observation
diff --git a/gpudrive/datatypes/trajectory.py b/gpudrive/datatypes/trajectory.py
index 99fbf72b8..eba306510 100644
--- a/gpudrive/datatypes/trajectory.py
+++ b/gpudrive/datatypes/trajectory.py
@@ -5,6 +5,13 @@
 TRAJ_LEN = 91  # Length of the logged trajectory
 
 
+def wrap_yaws(agent_headings: torch.Tensor) -> torch.Tensor:
+    """
+    Wrap an array of angles to the range [-pi, pi]
+    """
+    return ((agent_headings + torch.pi) % (2 * torch.pi)) - torch.pi
+
+
 def to_local_frame(
     global_pos_xy: torch.Tensor,
     ego_pos: torch.Tensor,
@@ -120,11 +127,87 @@ def length(self):
         return self.pos_xy.shape[2]
 
 
+@dataclass
+class VBDTrajectoryOnline:
+    """A class to represent the VBD predicted trajectories.
+    Initialized from VBD predictions.
+    Shape: (num_worlds, max_agents, traj_len, 5)
+
+    Attributes:
+        trajectories: Tensor of shape (num_worlds, max_agents, traj_len, 5) containing
+            position (x, y), heading (yaw), and velocity (vx, vy) for each timestep.
+    """
+
+    def __init__(
+        self, vbd_traj_tensor: torch.Tensor, mean_pos_xy: torch.Tensor
+    ):
+        """Initializes the VBD trajectory with a tensor."""
+        self.pos_x = vbd_traj_tensor[:, :, :, 0].unsqueeze(-1)
+        self.pos_y = vbd_traj_tensor[:, :, :, 1].unsqueeze(-1)
+        self.pos_xy = vbd_traj_tensor[:, :, :, :2]
+        self.yaw = wrap_yaws(vbd_traj_tensor[:, :, :, 2].unsqueeze(-1))
+        self.vel_x = vbd_traj_tensor[:, :, :, 3].unsqueeze(-1)
+        self.vel_y = vbd_traj_tensor[:, :, :, 4].unsqueeze(-1)
+        self.vel_xy = vbd_traj_tensor[:, :, :, 3:5]
+        self.ref_speed = self.comp_reference_speed()
+        # Assumption: All timesteps are valid (correct)
+        self.valids = torch.ones_like(self.pos_x, dtype=torch.int32)
+
+        self.mean_pos_xy = mean_pos_xy
+        self.mean_x = mean_pos_xy[:, 0]
+        self.mean_y = mean_pos_xy[:, 1]
+
+        self.demean_positions()
+
+    @classmethod
+    def from_tensor(
+        cls,
+        vbd_predictions,
+        mean_pos_xy,
+        backend="torch",
+        device="cuda",
+    ):
+        """Creates a VBDTrajectory from a tensor."""
+        if backend == "torch":
+            return cls(
+                vbd_predictions.clone().to(device),
+                mean_pos_xy.clone().to(device),
+            )
+        elif backend == "jax":
+            raise NotImplementedError("JAX backend not implemented yet.")
+
+    def comp_reference_speed(self):
+        """Returns the average speed of the trajectory."""
+        return torch.sqrt(self.vel_x**2 + self.vel_y**2)
+
+    @property
+    def length(self):
+        """Returns the length of the trajectory."""
+        return self.pos_xy.shape[2]
+
+    def demean_positions(self):
+        """
+        In GPUDrive, we center everything at zero. By default, the VBD
+        predictions are not centered at zero, so we demean the predicted trajectory.
+        """
+        # Reshape for broadcasting
+        mean_x_reshaped = self.mean_x.view(-1, 1, 1)
+        mean_y_reshaped = self.mean_y.view(-1, 1, 1)
+
+        # Apply to x and y coordinates
+        self.pos_xy[..., 0] -= mean_x_reshaped
+        self.pos_xy[..., 1] -= mean_y_reshaped
+
+        self.pos_x = self.pos_xy[..., 0].unsqueeze(-1)
+        self.pos_y = self.pos_xy[..., 1].unsqueeze(-1)
+
+
 @dataclass
 class VBDTrajectory:
     """A class to represent the VBD predicted trajectories.
     Initialized from `vbd_trajectory_tensor` (src/bindings.cpp).
     Shape: (num_worlds, max_agents, traj_len, 5)
+            where traj_len is 90 - initialization steps.
 
     Attributes:
         trajectories: Tensor of shape (num_worlds, max_agents, traj_len, 5) containing
@@ -136,12 +219,13 @@ def __init__(self, vbd_traj_tensor: torch.Tensor):
         self.pos_x = vbd_traj_tensor[:, :, :, 0].unsqueeze(-1)
         self.pos_y = vbd_traj_tensor[:, :, :, 1].unsqueeze(-1)
         self.pos_xy = vbd_traj_tensor[:, :, :, :2]
-        self.yaw = vbd_traj_tensor[:, :, :, 2].unsqueeze(-1)
+        self.yaw = wrap_yaws(vbd_traj_tensor[:, :, :, 2].unsqueeze(-1))
         self.vel_x = vbd_traj_tensor[:, :, :, 3].unsqueeze(-1)
         self.vel_y = vbd_traj_tensor[:, :, :, 4].unsqueeze(-1)
         self.vel_xy = vbd_traj_tensor[:, :, :, 3:5]
         self.ref_speed = self.comp_reference_speed()
         # Assumption: All timesteps are valid
+        # TODO: The first 10 timesteps come from the logs, so there may be invalid steps
         self.valids = torch.ones_like(self.pos_x, dtype=torch.int32)
 
     @classmethod
@@ -165,13 +249,3 @@ def comp_reference_speed(self):
     def length(self):
         """Returns the length of the trajectory."""
         return self.pos_xy.shape[2]
-
-    # def restore_mean(self, mean_x, mean_y):
-    #     """Reapplies the mean to revert back to the original coordinates."""
-    #     # Reshape for broadcasting
-    #     mean_x_reshaped = mean_x.view(-1, 1, 1)
-    #     mean_y_reshaped = mean_y.view(-1, 1, 1)
-
-    #     # Apply to x and y coordinates
-    #     self.trajectories[..., 0] += mean_x_reshaped
-    #     self.trajectories[..., 1] += mean_y_reshaped
diff --git a/gpudrive/env/config.py b/gpudrive/env/config.py
index 9e3af7609..b1368a440 100755
--- a/gpudrive/env/config.py
+++ b/gpudrive/env/config.py
@@ -61,7 +61,7 @@ class EnvConfig:
     dynamics_model: str = (
         "classic"  # Options: "classic", "bicycle", "delta_local", or "state"
     )
-    
+
     # Action space settings (if discretized)
     # Classic or Invertible Bicycle dynamics model
     action_space_steer_disc: int = 13
@@ -69,10 +69,16 @@ class EnvConfig:
     max_steer_angle: float = 1.57  # in radians: pi/2 = 1.57, pi/3 = 1.05
     max_accel_value: float = 4.0
     steer_actions: torch.Tensor = torch.round(
-        torch.linspace(-max_steer_angle, max_steer_angle, action_space_steer_disc), decimals=3
+        torch.linspace(
+            -max_steer_angle, max_steer_angle, action_space_steer_disc
+        ),
+        decimals=3,
     )
     accel_actions: torch.Tensor = torch.round(
-        torch.linspace(-max_accel_value, max_accel_value, action_space_accel_disc), decimals=3
+        torch.linspace(
+            -max_accel_value, max_accel_value, action_space_accel_disc
+        ),
+        decimals=3,
     )
     head_tilt_actions: torch.Tensor = torch.Tensor([0])
 
@@ -175,10 +181,8 @@ class EnvConfig:
     # Initialization mode
     init_mode: str = "all_non_trivial"  # Options: all_non_trivial, all_objects, all_valid, wosac_eval, wosac_train
 
-    # VBD model settings
-    use_vbd: bool = False
-    vbd_trajectory_weight: float = 0.01
-    vbd_in_obs: bool = False
+    # Versatile Behavior Diffusion (VBD)
+    vbd_model_path: str = "gpudrive/integrations/vbd/weights/epoch=18.ckpt"
 
 
 class SelectionDiscipline(Enum):
diff --git a/gpudrive/env/constants.py b/gpudrive/env/constants.py
index 3b71fd766..a48f5291e 100644
--- a/gpudrive/env/constants.py
+++ b/gpudrive/env/constants.py
@@ -19,7 +19,7 @@
 # Road graph constants
 MIN_RG_COORD = -1000
 MAX_RG_COORD = 1000
-MAX_ROAD_LINE_SEGMENT_LEN = 100
+MAX_ROAD_LINE_SEGMENT_LEN = 200
 MAX_ROAD_SCALE = 100
 
 # Feature shape constants
diff --git a/gpudrive/env/env_puffer.py b/gpudrive/env/env_puffer.py
index 8bd9ec3d3..0d2d740d8 100644
--- a/gpudrive/env/env_puffer.py
+++ b/gpudrive/env/env_puffer.py
@@ -198,8 +198,6 @@ def __init__(
         polyline_reduction_threshold=0.1,
         remove_non_vehicles=False,
         obs_radius=50.0,
-        use_vbd=False,
-        vbd_trajectory_weight=0.1,
         track_realism_metrics=False,
         track_n_worlds=2,
         render=False,
@@ -254,10 +252,6 @@ def __init__(
         self.track_realism_metrics = track_realism_metrics
         self.track_n_worlds = track_n_worlds
 
-        # VBD
-        self.vbd_trajectory_weight = vbd_trajectory_weight
-        self.use_vbd = use_vbd
-
         # Total number of agents across envs, including padding
         self.total_agents = self.max_cont_agents_per_env * self.num_worlds
 
@@ -298,8 +292,6 @@ def __init__(
             max_accel_value=max_accel_value,
             action_space_steer_disc=action_space_steer_disc,
             action_space_accel_disc=action_space_accel_disc,
-            use_vbd=use_vbd,
-            vbd_trajectory_weight=vbd_trajectory_weight,
         )
 
         render_config = RenderConfig(
diff --git a/gpudrive/env/env_torch.py b/gpudrive/env/env_torch.py
index 59e1014e0..8f21c16e6 100755
--- a/gpudrive/env/env_torch.py
+++ b/gpudrive/env/env_torch.py
@@ -13,6 +13,7 @@
 from gpudrive.datatypes.trajectory import (
     LogTrajectory,
     VBDTrajectory,
+    VBDTrajectoryOnline,
     to_local_frame,
 )
 from gpudrive.datatypes.roadgraph import (
@@ -28,11 +29,9 @@
 )
 from gpudrive.datatypes.metadata import Metadata
 from gpudrive.datatypes.info import Info
-
 from gpudrive.visualize.core import MatplotlibVisualizer
 from gpudrive.visualize.utils import img_from_fig
 from gpudrive.env.dataset import SceneDataLoader
-from gpudrive.utils.geometry import normalize_min_max
 
 from gpudrive.integrations.vbd.data.utils import process_scenario_data
 
@@ -60,10 +59,6 @@ def __init__(
         self.backend = backend
         self.max_num_agents_in_scene = self.config.max_num_agents_in_scene
 
-        self.world_time_steps = torch.zeros(
-            self.num_worlds, dtype=torch.short, device=self.device
-        )
-
         # Initialize reward weights tensor to None initially
         self.reward_weights_tensor = None
 
@@ -88,13 +83,13 @@ def __init__(
             self.cont_agent_mask.sum().item()
         )
 
-        self.setup_guidance()
-
         self.episode_len = self.config.episode_len
         self.step_in_world = (
             self.episode_len - self.sim.steps_remaining_tensor().to_torch()
         )
 
+        self.setup_guidance()
+
         if self.config.reward_type == "reward_conditioned":
             # Use default condition_mode from config or fall back to "random"
             condition_mode = getattr(self.config, "condition_mode", "random")
@@ -137,19 +132,48 @@ def __init__(
         )
 
     def setup_guidance(self):
-        """Configure the reference trajectory based on the guidance mode."""
+        """Configure the reference trajectory based on the guidance mode.
+
+        Returns:
+            reference_trajectory: The reference trajectory to guide the agent's
+            behavior. Shape: [num_worlds, max_agent_count, traj_len, feature_dim], where
+            features are assumed to be in the following order: [x, y, yaw, vx, vy]
+        """
         self.guidance_mode = self.config.guidance_mode
 
         if self.guidance_mode == "vbd_amortized":
+            # Use pre-generated VBD model predictions as guidance
             trajectory_tensor = self.sim.vbd_trajectory_tensor()
             self.reference_trajectory = VBDTrajectory.from_tensor(
                 trajectory_tensor, self.backend, self.device
             )
         elif self.guidance_mode == "vbd_online":
-            # TODO: Add support for 'vbd_online' mode
-            raise ValueError(
-                f"Unsupported guidance mode: {self.guidance_mode}."
+
+            self.init_steps = max(self.init_steps, 10)
+            print(
+                f"\n[Note] Guidance mode '{self.guidance_mode}' requires at least {self.init_steps} initialization steps to provide sufficient scene context for the diffusion model. Automatically setting simulator time to t = {self.init_steps}. \n"
+            )
+
+            # Load pre-trained Versatile Behavior Diffusion (VBD) model
+            self.vbd_model = self._load_vbd_model(
+                model_path=self.config.vbd_model_path
+            )
+            # Construct scene context dict for the VBD model
+            scene_context = self.construct_context(init_steps=self.init_steps)
+
+            print("Generating VBD predictions...\n")
+
+            # Query the model online for the reference trajectory
+            predicted = self.vbd_model.sample_denoiser(scene_context)
+
+            # Wrap predictions into a VBDTrajectoryOnline object
+            self.reference_trajectory = VBDTrajectoryOnline.from_tensor(
+                vbd_predictions=predicted["denoised_trajs"],
+                mean_pos_xy=self.sim.world_means_tensor().to_torch()[:, :2],
+                backend=self.backend,
+                device=self.device,
             )
+
         else:  # Default option is "log_replay"
             trajectory_tensor = self.sim.expert_trajectory_tensor()
             self.reference_trajectory = LogTrajectory.from_tensor(
@@ -163,26 +187,43 @@ def setup_guidance(self):
         # Length of the guidance trajectory
         self.reference_traj_len = self.reference_trajectory.length
 
-    def _initialize_vbd(self):
+    def _load_vbd_model(self, model_path):
         """
-        Initialize the Versatile Behavior Diffusion (VBD) model and related
-        components. Link: https://arxiv.org/abs/2404.02524.
-        When using amortized VBD, we don't need to run the model at runtime.
+        Load the Versatile Behavior Diffusion (VBD) weights from checkpoint.
+        """
+        from gpudrive.integrations.vbd.sim_agent.sim_actor import VBDTest
 
-        Args:
-            config: Configuration object containing VBD settings.
+        model = VBDTest.load_from_checkpoint(
+            model_path, torch.device(self.device)
+        )
+        model.reset_agent_length(self.max_cont_agents)
+        model.guidance_iter = 5
+        _ = model.eval()
+        return model
+
+    def construct_context(self, init_steps=10):
         """
-        self.use_vbd = self.config.use_vbd
-        self.vbd_trajectory_weight = self.config.vbd_trajectory_weight
-        if self.use_vbd:
-            self._load_vbd_trajectories()
-        else:
-            self.init_steps = self.config.init_steps
+        Construct a dictionary containing information from the first 10 steps (1s) of the scene.
+
+        This context data is used for the pre-trained VBD model, which infers
+        the most likely trajectory for the next 80 steps based on this context.
 
-            self.vbd_trajectories = None
+        Args:
+            init_steps (int): Number of steps to use for context. Default is 10.
 
-    def _generate_sample_batch(self, init_steps=10):
-        """Generate a sample batch for the VBD model."""
+        Returns:
+            dict: Dictionary containing context information with the following keys:
+                - 'agents_history': Past agent positions and states
+                - 'agents_interested': Agents marked for trajectory prediction
+                - 'agents_type': Type classification of each agent
+                - 'agents_future': Ground truth future states (if available)
+                - 'traffic_light_points': Location and state of traffic signals
+                - 'polylines': Road network geometry representation
+                - 'polylines_valid': Validity flags for polyline segments
+                - 'relations': Interaction relationships between agents
+                - 'agents_id': Unique identifiers for each agent
+                - 'anchors': Reference points
+        """
         means_xy = (
             self.sim.world_means_tensor().to_torch()[:, :2].to(self.device)
         )
@@ -224,9 +265,9 @@ def _generate_sample_batch(self, init_steps=10):
             backend=self.backend,
             device=self.device,
         )
-        sample_batch = process_scenario_data(
+        context_dict = process_scenario_data(
             max_controlled_agents=self.max_cont_agents,
-            controlled_agent_mask=self.cont_agent_mask,
+            controlled_agent_mask=self.cont_agent_mask.cpu(),
             global_agent_obs=global_agent_obs,
             global_road_graph=global_road_graph,
             log_trajectory=log_trajectory,
@@ -235,7 +276,8 @@ def _generate_sample_batch(self, init_steps=10):
             raw_agent_types=self.sim.info_tensor().to_torch()[:, :, 4],
             metadata=metadata,
         )
-        return sample_batch
+
+        return context_dict
 
     def _set_reward_weights(self, condition_mode="random", agent_type=None):
         """Set agent reward weights for all or specific environments.
@@ -427,8 +469,6 @@ def reset(
                 condition_mode=mode, agent_type=use_agent_type
             )
 
-        self.world_time_steps.fill_(self.init_steps)
-
         # Reset smoothness tracking for reset environments
         if env_idx_list is not None:
             reset_mask = torch.zeros(
@@ -646,11 +686,6 @@ def step_dynamics(self, actions):
             self.episode_len - self.sim.steps_remaining_tensor().to_torch()
         )
 
-        not_done_worlds = ~self.get_dones().any(
-            dim=1
-        )  # Check if any agent in world is done
-        self.world_time_steps[not_done_worlds] += 1
-
     def _apply_actions(self, actions):
         """Apply the actions to the simulator."""
 
@@ -1222,229 +1257,6 @@ def _get_bev_obs(self, mask=None):
         else:
             return bev.bev_segmentation_map.flatten(start_dim=2)
 
-    def _get_vbd_obs(self, mask=None):
-        """
-        Get ego-centric VBD trajectory observations for controlled agents.
-
-        Args:
-            mask: Optional mask to filter agents
-
-        Returns:
-            Tensor of ego-centric VBD trajectories
-        """
-        if not self.use_vbd or self.vbd_trajectories is None:
-            return torch.Tensor().to(self.device)
-
-        # Get current agent positions and orientations
-        agent_state = GlobalEgoState.from_tensor(
-            abs_self_obs_tensor=self.sim.absolute_self_observation_tensor(),
-            backend=self.backend,
-            device=self.device,
-        )
-
-        # Initialize output tensor
-        traj_feature_dim = (
-            self.vbd_trajectories.shape[2] * self.vbd_trajectories.shape[3]
-        )
-
-        if mask is not None:
-            # Count valid agents for output tensor size
-            valid_count = mask.sum().item()
-            ego_vbd_trajectories = torch.zeros(
-                (valid_count, traj_feature_dim), device=self.device
-            )
-
-            # Track which output index we're filling
-            out_idx = 0
-
-            # Process each world
-            for w in range(self.num_worlds):
-                # Get valid agent indices for this world
-                world_mask = mask[w]
-                agent_indices = torch.where(world_mask)[0]
-
-                if len(agent_indices) == 0:
-                    continue
-
-                # Extract ego positions and yaws for these agents
-                ego_pos_x = agent_state.pos_x[w, agent_indices]
-                ego_pos_y = agent_state.pos_y[w, agent_indices]
-                ego_yaw = agent_state.rotation_angle[w, agent_indices]
-
-                # Process each agent in this world
-                for i, agent_idx in enumerate(agent_indices):
-                    # Get global trajectory for this agent
-                    global_traj = self.vbd_trajectories[w, agent_idx]
-
-                    # Create 2D rotation matrix for this agent
-                    cos_yaw = torch.cos(ego_yaw[i])
-                    sin_yaw = torch.sin(ego_yaw[i])
-                    rotation_matrix = torch.tensor(
-                        [[cos_yaw, sin_yaw], [-sin_yaw, cos_yaw]],
-                        device=self.device,
-                    )
-
-                    # Transform positions using matrix multiplication
-                    pos_xy = global_traj[:, :2]
-                    ego_pos = torch.tensor(
-                        [ego_pos_x[i], ego_pos_y[i]], device=self.device
-                    ).reshape(1, 2)
-                    translated_pos = (
-                        pos_xy - ego_pos
-                    )  # Broadcasting to subtract from all timesteps
-                    rotated_pos = torch.matmul(
-                        translated_pos, rotation_matrix.T
-                    )
-
-                    # Transform velocities (only rotation, no translation)
-                    vel_xy = global_traj[:, 3:5]
-                    rotated_vel = torch.matmul(vel_xy, rotation_matrix.T)
-
-                    # Create transformed trajectory
-                    transformed_traj = torch.zeros_like(global_traj)
-                    transformed_traj[:, :2] = rotated_pos
-                    transformed_traj[:, 2] = (
-                        global_traj[:, 2] - ego_yaw[i]
-                    )  # Adjust heading
-                    transformed_traj[:, 3:5] = rotated_vel
-
-                    # Flatten and add to output
-                    ego_vbd_trajectories[out_idx] = transformed_traj.reshape(
-                        -1
-                    )
-                    out_idx += 1
-
-            if self.config.norm_obs:
-                ego_vbd_trajectories = self._normalize_vbd_obs(
-                    ego_vbd_trajectories, self.vbd_trajectories.shape[2]
-                )
-
-            return ego_vbd_trajectories
-
-        else:
-            # Without mask, process all agents in all worlds
-            ego_vbd_trajectories = torch.zeros(
-                (self.num_worlds, self.max_agent_count, traj_feature_dim),
-                device=self.device,
-            )
-
-            # Process each world
-            for w in range(self.num_worlds):
-                # Get controlled agent indices for this world
-                valid_mask = self.cont_agent_mask[w]
-                world_agent_indices = torch.where(valid_mask)[0]
-
-                if len(world_agent_indices) == 0:
-                    continue
-
-                # Extract ego positions and yaws
-                ego_pos_x = agent_state.pos_x[w]
-                ego_pos_y = agent_state.pos_y[w]
-                ego_yaw = agent_state.rotation_angle[w]
-
-                # Process each agent in this world
-                for agent_idx in world_agent_indices:
-                    # Get global trajectory
-                    global_traj = self.vbd_trajectories[w, agent_idx]
-
-                    # Create 2D rotation matrix for this agent
-                    cos_yaw = torch.cos(ego_yaw[agent_idx])
-                    sin_yaw = torch.sin(ego_yaw[agent_idx])
-                    rotation_matrix = torch.tensor(
-                        [[cos_yaw, sin_yaw], [-sin_yaw, cos_yaw]],
-                        device=self.device,
-                    )
-
-                    # Transform positions
-                    pos_xy = global_traj[:, :2]
-                    ego_pos = torch.tensor(
-                        [ego_pos_x[agent_idx], ego_pos_y[agent_idx]],
-                        device=self.device,
-                    ).reshape(1, 2)
-                    translated_pos = pos_xy - ego_pos
-                    rotated_pos = torch.matmul(
-                        translated_pos, rotation_matrix.T
-                    )
-
-                    # Transform velocities
-                    vel_xy = global_traj[:, 3:5]
-                    rotated_vel = torch.matmul(vel_xy, rotation_matrix.T)
-
-                    # Create transformed trajectory
-                    transformed_traj = torch.zeros_like(global_traj)
-                    transformed_traj[:, :2] = rotated_pos
-                    transformed_traj[:, 2] = (
-                        global_traj[:, 2] - ego_yaw[agent_idx]
-                    )
-                    transformed_traj[:, 3:5] = rotated_vel
-
-                    # Flatten and add to output
-                    ego_vbd_trajectories[
-                        w, agent_idx
-                    ] = transformed_traj.reshape(-1)
-
-            if self.config.norm_obs:
-                ego_vbd_trajectories = self._normalize_vbd_obs(
-                    ego_vbd_trajectories, self.vbd_trajectories.shape[2]
-                )
-
-            return ego_vbd_trajectories
-
-    def _normalize_vbd_obs(self, trajectories_flat, traj_len):
-        """
-        Normalize flattened VBD trajectory values to be between -1 and 1, with clipping.
-
-        Args:
-            trajectories_flat: Flattened tensor containing trajectory data
-            traj_len: Number of trajectory steps
-
-        Returns:
-            Normalized flattened trajectories tensor
-        """
-        # Get original shape for proper reshaping
-        original_shape = trajectories_flat.shape
-
-        # Calculate feature dimension
-        feature_dim = 5  # x, y, yaw, vel_x, vel_y
-
-        # Reshape to separate the features
-        if len(original_shape) == 2:  # (num_agents, flattened_features)
-            traj_features = trajectories_flat.reshape(
-                -1, traj_len, feature_dim
-            )
-        else:  # (num_worlds, max_agents, flattened_features)
-            traj_features = trajectories_flat.reshape(
-                original_shape[0], original_shape[1], traj_len, feature_dim
-            )
-
-        # Normalize each feature
-        # x, y positions
-        traj_features[..., 0] = normalize_min_max(
-            tensor=traj_features[..., 0],
-            min_val=constants.MIN_REL_GOAL_COORD,
-            max_val=constants.MAX_REL_GOAL_COORD,
-        )
-        traj_features[..., 1] = normalize_min_max(
-            tensor=traj_features[..., 1],
-            min_val=constants.MIN_REL_GOAL_COORD,
-            max_val=constants.MAX_REL_GOAL_COORD,
-        )
-
-        # Normalize yaw angle
-        traj_features[..., 2] = (
-            traj_features[..., 2] / constants.MAX_ORIENTATION_RAD
-        )
-
-        # Normalize velocities
-        traj_features[..., 3] = traj_features[..., 3] / constants.MAX_SPEED
-        traj_features[..., 4] = traj_features[..., 4] / constants.MAX_SPEED
-
-        # Clip all values to the [-1, 1] range
-        traj_features = torch.clamp(traj_features, min=-1.0, max=1.0)
-
-        # Reshape back to original format
-        return traj_features.reshape(original_shape)
-
     def get_obs(self, mask=None):
         """Get observation: Combine different types of environment information into a single tensor.
         Returns:
@@ -1455,29 +1267,15 @@ def get_obs(self, mask=None):
         road_map_observations = self._get_road_map_obs(mask)
         guidance_obs = self._get_guidance(mask)
 
-        if self.config.use_vbd and self.config.vbd_in_obs:
-            # Add ego-centric VBD trajectories
-            vbd_observations = self._get_vbd_obs(mask)
-
-            obs = torch.cat(
-                (
-                    ego_states,
-                    partner_observations,
-                    road_map_observations,
-                    vbd_observations,
-                ),
-                dim=-1,
-            )
-        else:
-            obs = torch.cat(
-                (
-                    ego_states,
-                    partner_observations,
-                    road_map_observations,
-                    guidance_obs,
-                ),
-                dim=-1,
-            )
+        obs = torch.cat(
+            (
+                ego_states,
+                partner_observations,
+                road_map_observations,
+                guidance_obs,
+            ),
+            dim=-1,
+        )
 
         return obs
 
@@ -1487,27 +1285,6 @@ def get_controlled_agents_mask(self):
             self.sim.controlled_state_tensor().to_torch().clone() == 1
         ).squeeze(axis=2)
 
-    def advance_sim_with_log_playback(self, init_steps=0):
-        """Advances the simulator by stepping the objects with the logged human trajectories.
-
-        Args:
-            init_steps (int): Number of warmup steps.
-        """
-        if init_steps >= self.config.episode_len:
-            raise ValueError(
-                "The length of the expert trajectory is 91,"
-                f"so init_steps = {init_steps} should be < than 91."
-            )
-
-        self.init_frames = []
-
-        self.log_playback_traj, _, _, _ = self.get_expert_actions()
-
-        for time_step in range(init_steps):
-            self.step_dynamics(
-                actions=self.log_playback_traj[:, :, time_step, :]
-            )
-
     def remove_agents_by_id(
         self, perc_to_rmv_per_scene, remove_controlled_agents=True
     ):
@@ -1701,7 +1478,7 @@ def get_scenario_ids(self):
 
     env_config = EnvConfig(
         guidance=True,
-        guidance_mode="log_replay",  # "vbd_amortized"
+        guidance_mode="vbd_online",  # Options: "log_replay", "vbd_amortized"
         add_reference_pos_xy=True,
         add_reference_speed=True,
         add_reference_heading=True,
@@ -1744,18 +1521,6 @@ def get_scenario_ids(self):
 
     highlight_agent = torch.where(control_mask[env_idx, :])[0][0].item()
 
-    agent_positions = []
-    init_state = GlobalEgoState.from_tensor(
-        env.sim.absolute_self_observation_tensor(),
-        device=env.device,
-    )
-    means_xy = env.sim.world_means_tensor().to_torch()[:, :2].to(env.device)
-    init_state.restore_mean(mean_x=means_xy[:, 0], mean_y=means_xy[:, 1])
-    agent_positions.append(init_state.pos_xy[env_idx, highlight_agent])
-
-    print(f"Highlighted agent: {highlight_agent}")
-    print(f"Position: {agent_positions[-1]}")
-
     for t in range(env.init_steps, env.init_steps + 10):
         print(f"Step: {t+1}")
 
@@ -1763,15 +1528,6 @@ def get_scenario_ids(self):
         expert_actions, _, _, _ = env.get_expert_actions()
         env.step_dynamics(expert_actions[:, :, t, :])
 
-        current_state = GlobalEgoState.from_tensor(
-            env.sim.absolute_self_observation_tensor(),
-            device=env.device,
-        )
-        current_state.restore_mean(
-            mean_x=means_xy[:, 0], mean_y=means_xy[:, 1]
-        )
-        agent_positions.append(current_state.pos_xy[env_idx, highlight_agent])
-
         # Make video
         sim_states = env.vis.plot_simulator_state(
             env_indices=[env_idx],
@@ -1790,6 +1546,8 @@ def get_scenario_ids(self):
             ),
         )
 
+        print(env.reference_trajectory.pos_xy[control_mask].max())
+
         sim_frames.append(img_from_fig(sim_states[0]))
         agent_obs_frames.append(img_from_fig(agent_obs))
 
@@ -1798,7 +1556,6 @@ def get_scenario_ids(self):
 
         print(f"A_t: {expert_actions[env_idx, highlight_agent, t, :]}")
         print(f"R_t+1: {reward[env_idx, highlight_agent]}")
-        print(f"Position: {agent_positions[-1]}")
 
         done = env.get_dones()
         info = env.get_infos()
diff --git a/gpudrive/integrations/puffer/ppo.py b/gpudrive/integrations/puffer/ppo.py
index 70cad1461..ea613ae84 100644
--- a/gpudrive/integrations/puffer/ppo.py
+++ b/gpudrive/integrations/puffer/ppo.py
@@ -40,8 +40,6 @@ def create(config, vecenv, policy, optimizer=None, wandb=None):
 
     utilization = Utilization()
     msg = f"Model Size: {abbreviate(count_params(policy))} parameters"
-    if vecenv.use_vbd:
-        msg += f" | Using VBD"
     print_dashboard(
         config.env, utilization, 0, 0, profile, losses, {}, msg, clear=True
     )
diff --git a/gpudrive/integrations/vbd/data/amortize.py b/gpudrive/integrations/vbd/data/amortize.py
index 35fa375d6..394501adc 100644
--- a/gpudrive/integrations/vbd/data/amortize.py
+++ b/gpudrive/integrations/vbd/data/amortize.py
@@ -5,9 +5,9 @@
 It uses a sliding window approach to generate trajectories in chunks.
 
 Usage:
-    python amortize.py 
-        --model_path /path/to/vbd/model 
-        --input_dir /path/to/input/json/files 
+    python amortize.py
+        --model_path /path/to/vbd/model
+        --input_dir /path/to/input/json/files
         --output_dir /path/to/output/json/files
         --batch_size 8
         --window_size 10
@@ -28,6 +28,7 @@
 from gpudrive.datatypes.observation import GlobalEgoState
 from gpudrive.integrations.vbd.sim_agent.sim_actor import VBDTest
 
+
 def load_vbd_model(model_path, device="cpu", max_cont_agents=64):
     """Load the VBD model from a checkpoint"""
     model = VBDTest.load_from_checkpoint(model_path, torch.device(device))
@@ -36,113 +37,184 @@ def load_vbd_model(model_path, device="cpu", max_cont_agents=64):
     _ = model.eval()
     return model
 
+
 def main():
     parser = argparse.ArgumentParser(description="VBD Amortization Script")
-    parser.add_argument("--model_path", help="Path to the VBD model checkpoint", default="gpudrive/integrations/vbd/weights/epoch=18.ckpt")
-    parser.add_argument("--input_dir", help="Directory containing input JSON files", default="data/processed/examples")
-    parser.add_argument("--output_dir", help="Directory for output JSON files", default="data/processed/vbd")
-    parser.add_argument("--batch_size", type=int, default=1, help="Batch size for processing")
-    parser.add_argument("--window_size", type=int, default=10, help="Size of sliding window")
-    parser.add_argument("--total_steps", type=int, default=91, help="Total number of steps to generate")
-    parser.add_argument("--num_scenes", type=int, default=4, help="Number of scenes to process")
+    parser.add_argument(
+        "--model_path",
+        help="Path to the VBD model checkpoint",
+        default="gpudrive/integrations/vbd/weights/epoch=18.ckpt",
+    )
+    parser.add_argument(
+        "--input_dir",
+        help="Directory containing input JSON files",
+        default="data/processed/wosac/validation_json_100",
+    )
+    parser.add_argument(
+        "--output_dir",
+        help="Directory for output JSON files",
+        default="data/processed/wosac/validation_json_100",
+    )
+    parser.add_argument(
+        "--batch_size", type=int, default=10, help="Batch size for processing"
+    )
+    parser.add_argument(
+        "--window_size", type=int, default=10, help="Size of sliding window"
+    )
+    parser.add_argument(
+        "--total_steps",
+        type=int,
+        default=91,
+        help="Total number of steps to generate",
+    )
+    parser.add_argument(
+        "--num_scenes",
+        type=int,
+        default=100,
+        help="Number of scenes to process",
+    )
     args = parser.parse_args()
-    
+
     # Always use CPU device
     device = "cpu"
     MAX_CONTROLLED_AGENTS = 64
     print(f"Loading VBD model on {device}...")
     vbd_model = load_vbd_model(args.model_path, device, MAX_CONTROLLED_AGENTS)
-    
+
     # Find all JSON files in the input directory
     json_files = list(Path(args.input_dir).glob("**/*.json"))
-    
+
     print(f"Found {len(json_files)} JSON files to process")
-    
-    #Init GPUDrive env
-    INIT_STEPS = 11
+
+    # Init GPUDrive env
+    INIT_STEPS = 10
     env_config = EnvConfig(
-        init_steps=INIT_STEPS, # Warmup period
-        dynamics_model="state", # Use state-based dynamics model
-        dist_to_goal_threshold=1e-5, # Trick to make sure the agents don't disappear when they reach the goal
-        init_mode = 'wosac_eval',
+        init_steps=INIT_STEPS,  # Warmup period
+        dynamics_model="state",  # Use state-based dynamics model
+        dist_to_goal_threshold=1e-5,  # Trick to make sure the agents don't disappear when they reach the goal
+        init_mode="wosac_eval",
         max_controlled_agents=MAX_CONTROLLED_AGENTS,
-        goal_behavior='ignore'
+        goal_behavior="ignore",
     )
-            
+
     # Make env
     gpudrive_env = GPUDriveTorchEnv(
         config=env_config,
-        data_loader = SceneDataLoader(
+        data_loader=SceneDataLoader(
             root=args.input_dir,
             batch_size=args.batch_size,
             dataset_size=args.num_scenes,
+            file_prefix="",
         ),
         render_config=RenderConfig(render_3d=True),
-        max_cont_agents=MAX_CONTROLLED_AGENTS, # Maximum number of agents to control per scene
+        max_cont_agents=MAX_CONTROLLED_AGENTS,  # Maximum number of agents to control per scene
         device=device,
     )
 
     # Final VBD trajectories to write
-    output_trajectories = torch.zeros(gpudrive_env.num_worlds, gpudrive_env.max_agent_count, env_config.episode_len, 5)
+    output_trajectories = torch.zeros(
+        gpudrive_env.num_worlds,
+        gpudrive_env.max_agent_count,
+        env_config.episode_len,
+        5,
+    )
 
     # Save init steps from logs
     log_trajectory = LogTrajectory.from_tensor(
         gpudrive_env.sim.expert_trajectory_tensor(),
         num_worlds=gpudrive_env.num_worlds,
         max_agents=gpudrive_env.max_agent_count,
-        device=device
+        device=device,
     )
-    
-    output_trajectories[:, :, :INIT_STEPS, :2] = log_trajectory.pos_xy[:, :, :INIT_STEPS]
-    output_trajectories[:, :, :INIT_STEPS, 2] = log_trajectory.yaw[:, :, :INIT_STEPS, 0]
-    output_trajectories[:, :, :INIT_STEPS, 3:] = log_trajectory.vel_xy[:, :, :INIT_STEPS]
+
+    output_trajectories[:, :, :INIT_STEPS, :2] = log_trajectory.pos_xy[
+        :, :, :INIT_STEPS
+    ]
+    output_trajectories[:, :, :INIT_STEPS, 2] = log_trajectory.yaw[
+        :, :, :INIT_STEPS, 0
+    ]
+    output_trajectories[:, :, :INIT_STEPS, 3:] = log_trajectory.vel_xy[
+        :, :, :INIT_STEPS
+    ]
 
     # Action tensor to step through simulation
-    predicted_actions = torch.zeros((gpudrive_env.num_worlds, gpudrive_env.max_agent_count, gpudrive_env.episode_len - INIT_STEPS, 10))
+    predicted_actions = torch.zeros(
+        (
+            gpudrive_env.num_worlds,
+            gpudrive_env.max_agent_count,
+            gpudrive_env.episode_len - INIT_STEPS,
+            10,
+        )
+    )
 
     # World means for VBD outputs
-    world_means = gpudrive_env.sim.world_means_tensor().to_torch()[:, :2].to(device)
+    world_means = (
+        gpudrive_env.sim.world_means_tensor().to_torch()[:, :2].to(device)
+    )
 
     for _ in range(int(args.num_scenes / args.batch_size)):
         # Generate VBD input
-        gpudrive_sample_batch = gpudrive_env._generate_sample_batch()
+        scene_context = gpudrive_env.construct_context(
+            init_steps=gpudrive_env.init_steps
+        )
         # Controlled agent mask
-        world_agent_indices = gpudrive_sample_batch['agents_id']
+        world_agent_indices = scene_context["agents_id"]
 
         # Generate VBD output
-        predictions = vbd_model.sample_denoiser(gpudrive_sample_batch)
+        predictions = vbd_model.sample_denoiser(scene_context)
         vbd_output = predictions["denoised_trajs"].to(device).detach()
 
         for i in range(gpudrive_env.num_worlds):
             # Get controlled agent indices for this world
-            valid_mask = world_agent_indices[i] >= 0  # Boolean mask of valid indices
-            valid_world_indices = world_agent_indices[i][valid_mask]  # Filtered tensor
-            
+            valid_mask = (
+                world_agent_indices[i] >= 0
+            )  # Boolean mask of valid indices
+            valid_world_indices = world_agent_indices[i][
+                valid_mask
+            ]  # Filtered tensor
+
             # Populate predicted actions
-            predicted_actions[i, valid_world_indices, :, :2] = vbd_output[i, valid_world_indices, :, :2] - world_means[i].view(1, 1, 2)
-            predicted_actions[i, valid_world_indices, :, 3] = vbd_output[i, valid_world_indices, :, 2]
-            predicted_actions[i, valid_world_indices, :, 4:6] = vbd_output[i, valid_world_indices, :, 3:]
+            predicted_actions[i, valid_world_indices, :, :2] = vbd_output[
+                i, valid_world_indices, :, :2
+            ] - world_means[i].view(1, 1, 2)
+            predicted_actions[i, valid_world_indices, :, 3] = vbd_output[
+                i, valid_world_indices, :, 2
+            ]
+            predicted_actions[i, valid_world_indices, :, 4:6] = vbd_output[
+                i, valid_world_indices, :, 3:
+            ]
 
             # Populate output trajectories
-            output_trajectories[i, valid_world_indices, INIT_STEPS:, :2] = vbd_output[i, valid_world_indices, :, :2] - world_means[i].view(1, 1, 2)
-            output_trajectories[i, valid_world_indices, INIT_STEPS:, 2:] = vbd_output[i, valid_world_indices, :, 2:]
+            output_trajectories[
+                i, valid_world_indices, INIT_STEPS:, :2
+            ] = vbd_output[i, valid_world_indices, :, :2] - world_means[
+                i
+            ].view(
+                1, 1, 2
+            )
+            output_trajectories[
+                i, valid_world_indices, INIT_STEPS:, 2:
+            ] = vbd_output[i, valid_world_indices, :, 2:]
 
         # Save to each file's json
         index_to_id = GlobalEgoState.from_tensor(
-            gpudrive_env.sim.absolute_self_observation_tensor(),
-            device=device
+            gpudrive_env.sim.absolute_self_observation_tensor(), device=device
         ).id
-        
-        filenames = gpudrive_env.get_env_filenames()
-        
+
+        filenames = gpudrive_env.get_scenario_ids()
+
         for i in range(gpudrive_env.num_worlds):
-            filename = filenames[i]
-            with open(os.path.join(args.input_dir, filename), "r") as f:
+            filename = f"{filenames[i]}.json"
+            with open(f"{args.input_dir}/{filename}", "r") as f:
                 data = json.load(f)
-            
-            valid_mask = world_agent_indices[i] >= 0  # Boolean mask of valid indices
-            valid_world_indices = world_agent_indices[i][valid_mask]  # Filtered tensor
-        
+
+            valid_mask = (
+                world_agent_indices[i] >= 0
+            )  # Boolean mask of valid indices
+            valid_world_indices = world_agent_indices[i][
+                valid_mask
+            ]  # Filtered tensor
+
             # Find object with correct id
             for j in valid_world_indices:
                 id = index_to_id[i, j]
@@ -150,7 +222,9 @@ def main():
                 for obj in data["objects"]:
                     if obj["id"] == id:
                         # Add VBD output to JSON
-                        obj["vbd_trajectory"] = output_trajectories[i, j].cpu().numpy().tolist()
+                        obj["vbd_trajectory"] = (
+                            output_trajectories[i, j].cpu().numpy().tolist()
+                        )
                         break
 
             # Write to output directory
@@ -158,15 +232,20 @@ def main():
             os.makedirs(os.path.dirname(output_file), exist_ok=True)
             with open(output_file, "w") as f:
                 json.dump(data, f, indent=4)
-        
+
         # Load next batch of scenes
         try:
             gpudrive_env.swap_data_batch()
         except Exception as e:
             print(f"Reached end of dataset: {e}")
             break
-    
+
     print("VBD amortization complete!")
 
+
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()
+
+    """
+    python gpudrive/integrations/vbd/data/amortize.py --input_dir data/processed/wosac/validation_json_100 --output_dir data/processed/wosac/validation_json_100 --batch_size 8 --window_size 10 --total_steps 91
+    """
diff --git a/gpudrive/integrations/vbd/data/utils.py b/gpudrive/integrations/vbd/data/utils.py
index 913713954..de37bb1bd 100644
--- a/gpudrive/integrations/vbd/data/utils.py
+++ b/gpudrive/integrations/vbd/data/utils.py
@@ -34,11 +34,11 @@ def filter_topk_roadgraph_points(global_road_graph, reference_points, topk):
         raise NotImplementedError("Not enough points in roadgraph.")
 
     elif topk < global_road_graph.num_points:
-        roadgraph_xy = np.asarray(global_road_graph.xy[0])
+        roadgraph_xy = np.asarray(global_road_graph.xy[0].cpu())
         distances = np.linalg.norm(
             reference_points[..., None, :] - roadgraph_xy, axis=-1
         )
-        valid_distances = np.where(global_road_graph.id > 0, distances, float("inf"))
+        valid_distances = np.where(global_road_graph.id.cpu() > 0, distances, float("inf"))
         top_idx = np.argpartition(valid_distances, topk, axis=-1)[..., :topk]
 
         # Gather the topk points by slicing along the indices
@@ -206,9 +206,9 @@ def process_agents_vectorized(num_worlds, max_cont_agents, init_steps, global_ag
                     global_agent_obs.vehicle_width[w, a].repeat(init_steps + 1),
                     global_agent_obs.vehicle_height[w, a].repeat(init_steps + 1),
                 ],
-            ).numpy()
+            ).cpu().numpy()
             
-            mask = log_trajectory.valids[w, a, :init_steps+1].numpy()
+            mask = log_trajectory.valids[w, a, :init_steps+1].cpu().numpy()
             agents_history[w, i] *= mask
             
             agents_future[w, i] = torch.column_stack(
@@ -219,9 +219,9 @@ def process_agents_vectorized(num_worlds, max_cont_agents, init_steps, global_ag
                     log_trajectory.vel_xy[w, a, init_steps:, 0],
                     log_trajectory.vel_xy[w, a, init_steps:, 1],
                 ],
-            ).numpy()
+            ).cpu().numpy()
             
-            mask = log_trajectory.valids[w, a, init_steps:].numpy()
+            mask = log_trajectory.valids[w, a, init_steps:].cpu().numpy()
             agents_future[w, i] *= mask
     
     # Map agent types for all worlds at once (this is vectorized)
@@ -261,11 +261,11 @@ def process_world_roadgraph(global_road_graph, world_idx, agents_history, agents
                     sorted_map_ids.append(map_ids[j][i])
     
     # Extract roadgraph properties
-    roadgraph_points_x = np.asarray(global_road_graph.x[world_idx])
-    roadgraph_points_y = np.asarray(global_road_graph.y[world_idx])
-    roadgraph_points_heading = np.asarray(global_road_graph.orientation[world_idx])
-    roadgraph_points_types = np.asarray(global_road_graph.vbd_type[world_idx])
-    road_graph_points_ids = np.asarray(global_road_graph.id[world_idx])
+    roadgraph_points_x = np.asarray(global_road_graph.x[world_idx].cpu())
+    roadgraph_points_y = np.asarray(global_road_graph.y[world_idx].cpu())
+    roadgraph_points_heading = np.asarray(global_road_graph.orientation[world_idx].cpu())
+    roadgraph_points_types = np.asarray(global_road_graph.vbd_type[world_idx].cpu())
+    road_graph_points_ids = np.asarray(global_road_graph.id[world_idx].cpu())
     
     # Build polylines
     polylines = []
diff --git a/gpudrive/integrations/vbd/notebooks/03_vbd_output_comparison.ipynb b/gpudrive/integrations/vbd/notebooks/03_vbd_output_comparison.ipynb
index aabac353f..750851d8f 100644
--- a/gpudrive/integrations/vbd/notebooks/03_vbd_output_comparison.ipynb
+++ b/gpudrive/integrations/vbd/notebooks/03_vbd_output_comparison.ipynb
@@ -12,48 +12,18 @@
    "execution_count": 1,
    "metadata": {},
    "outputs": [
-    {
-     "ename": "RuntimeError",
-     "evalue": "module compiled against ABI version 0x1000009 but this version of numpy is 0x2000000",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mRuntimeError\u001b[39m                              Traceback (most recent call last)",
-      "\u001b[31mRuntimeError\u001b[39m: module compiled against ABI version 0x1000009 but this version of numpy is 0x2000000"
-     ]
-    },
-    {
-     "ename": "RuntimeError",
-     "evalue": "module compiled against ABI version 0x1000009 but this version of numpy is 0x2000000",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mRuntimeError\u001b[39m                              Traceback (most recent call last)",
-      "\u001b[31mRuntimeError\u001b[39m: module compiled against ABI version 0x1000009 but this version of numpy is 0x2000000"
-     ]
-    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-04-15 14:47:51.138446: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+      "2025-04-30 14:32:10.285208: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
       "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1744742871.227262   10057 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1744742871.257112   10057 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "W0000 00:00:1744742871.450339   10057 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
-      "W0000 00:00:1744742871.450399   10057 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
-      "W0000 00:00:1744742871.450403   10057 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
-      "W0000 00:00:1744742871.450406   10057 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n"
-     ]
-    },
-    {
-     "ename": "RuntimeError",
-     "evalue": "module compiled against ABI version 0x1000009 but this version of numpy is 0x2000000",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mRuntimeError\u001b[39m                              Traceback (most recent call last)",
-      "\u001b[31mRuntimeError\u001b[39m: module compiled against ABI version 0x1000009 but this version of numpy is 0x2000000"
+      "E0000 00:00:1746037930.293583  687951 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1746037930.295801  687951 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "W0000 00:00:1746037930.302638  687951 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
+      "W0000 00:00:1746037930.302648  687951 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
+      "W0000 00:00:1746037930.302649  687951 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
+      "W0000 00:00:1746037930.302650  687951 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n"
      ]
     }
    ],
@@ -423,7 +393,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/gpudrive/networks/late_fusion.py b/gpudrive/networks/late_fusion.py
index 98b18fc57..207e5a22c 100644
--- a/gpudrive/networks/late_fusion.py
+++ b/gpudrive/networks/late_fusion.py
@@ -95,7 +95,6 @@ def __init__(
         self.num_modes = 3  # Ego, partner, road graph
         self.dropout = dropout
         self.act_func = nn.Tanh() if act_func == "tanh" else nn.GELU()
-        self.vbd_in_obs = False
 
         # Ego state base fields
         self.ego_state_idx = constants.EGO_FEAT_DIM
@@ -110,7 +109,6 @@ def __init__(
             if "add_reference_pos_xy" in self.config:
                 self.ego_state_idx += 2 * 91
 
-            self.vbd_in_obs = self.config.vbd_in_obs
         self.partner_obs_idx = self.ego_state_idx + (
             constants.PARTNER_FEAT_DIM * self.max_observable_agents
         )
@@ -148,17 +146,6 @@ def __init__(
             pufferlib.pytorch.layer_init(nn.Linear(input_dim, input_dim)),
         )
 
-        if self.vbd_in_obs:
-            self.vbd_embed = nn.Sequential(
-                pufferlib.pytorch.layer_init(
-                    nn.Linear(self.vbd_size, input_dim)
-                ),
-                nn.LayerNorm(input_dim),
-                self.act_func,
-                nn.Dropout(self.dropout),
-                pufferlib.pytorch.layer_init(nn.Linear(input_dim, input_dim)),
-            )
-
         self.shared_embed = nn.Sequential(
             nn.Linear(self.input_dim * self.num_modes, self.hidden_dim),
             nn.Dropout(self.dropout),

From f9650666c11c5a7f245878bd47a7508f2b1b54a7 Mon Sep 17 00:00:00 2001
From: Daphne Cornelisse <33460159+daphne-cornelisse@users.noreply.github.com>
Date: Fri, 2 May 2025 12:08:35 -0400
Subject: [PATCH 06/38] Integrate optional smoothening pipeline for guidance
 data. (#434)

---
 .../figures_data/reference_log_replay_raw.npy |  Bin 0 -> 423824 bytes
 .../reference_log_replay_smooth.npy           |  Bin 0 -> 423824 bytes
 .../notebooks/guidance_data_analysis.ipynb    | 9781 ++++++++++++++++-
 .../eval/notebooks/obtain_guidance_data.py    |    9 +-
 gpudrive/env/config.py                        |    2 +
 gpudrive/env/env_torch.py                     |   13 +-
 gpudrive/utils/preprocess.py                  |  136 +
 7 files changed, 9919 insertions(+), 22 deletions(-)
 create mode 100644 examples/eval/figures_data/reference_log_replay_raw.npy
 create mode 100644 examples/eval/figures_data/reference_log_replay_smooth.npy
 create mode 100644 gpudrive/utils/preprocess.py

diff --git a/examples/eval/figures_data/reference_log_replay_raw.npy b/examples/eval/figures_data/reference_log_replay_raw.npy
new file mode 100644
index 0000000000000000000000000000000000000000..c6a4202592ebb8b5588a3e4fa709473f3e0ecd97
GIT binary patch
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diff --git a/examples/eval/notebooks/guidance_data_analysis.ipynb b/examples/eval/notebooks/guidance_data_analysis.ipynb
index 708b09109..7b91ec50f 100644
--- a/examples/eval/notebooks/guidance_data_analysis.ipynb
+++ b/examples/eval/notebooks/guidance_data_analysis.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -55,12 +55,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
     "DATASET = \"data/processed/wosac/validation_json_100\"\n",
-    "FIGURES_DIR = \"examples/eval/figures\""
+    "FIGURES_DIR = \"examples/eval/figures\"\n",
+    "DATA_FOLDER = \"examples/eval/figures_data/\""
    ]
   },
   {
@@ -72,19 +73,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['data/processed/wosac/validation_json_100/12858b7f79387840.json', 'data/processed/wosac/validation_json_100/14929c51c7298df1.json']\n"
+      "['data/processed/wosac/validation_json_100/12858b7f79387840.json', 'data/processed/wosac/validation_json_100/14929c51c7298df1.json', 'data/processed/wosac/validation_json_100/14d7108a34259337.json', 'data/processed/wosac/validation_json_100/17c4e1e5a9665445.json']\n"
      ]
     }
    ],
    "source": [
-    "GUIDANCE_MODE = \"vbd_amortized\"\n",
+    "GUIDANCE_MODE = \"log_replay\"\n",
     "\n",
     "env_config = EnvConfig(\n",
     "    dynamics_model=\"classic\",\n",
@@ -95,12 +96,13 @@
     "    add_reference_speed=True,\n",
     "    add_reference_pos_xy=True,\n",
     "    init_mode=\"wosac_train\",\n",
+    "    smoothen_trajectory=True,\n",
     ")\n",
     "render_config = RenderConfig()\n",
     "\n",
     "train_loader = SceneDataLoader(\n",
     "    root=DATASET,\n",
-    "    batch_size=2,\n",
+    "    batch_size=4,\n",
     "    dataset_size=100,\n",
     "    sample_with_replacement=False,\n",
     "    shuffle=False,\n",
@@ -130,42 +132,9787 @@
     "# np.save(f\"reference_{GUIDANCE_MODE}.npy\", reference_traj_np)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([4, 64, 91, 2])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "env.reference_trajectory.pos_xy[0, 0, :, 0]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Q1. Are the distributions from different modes (`vbd_online`, `log_replay` and `vbd_amortized`) on the same scale?\n",
+    "### Smoothening trajectory data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ref_log_replay_raw = np.load(f\"{DATA_FOLDER}reference_log_replay_raw.npy\")\n",
+    "ref_log_replay_smooth = np.load(f\"{DATA_FOLDER}reference_log_replay_smooth.npy\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_trajectories(raw, smooth, agent_idx):\n",
+    "    \n",
+    "    valid = raw[agent_idx, :, 5].astype(bool)\n",
+    "    \n",
+    "    fig, axs = plt.subplots(1, 3, figsize=(10, 3))\n",
+    "    axs[0].set_title(f\"Global position (x, y); agent {agent_idx}\")\n",
+    "    axs[0].plot(raw[agent_idx, :, 0][valid], raw[agent_idx, :, 1][valid], color='b')\n",
+    "    axs[0].plot(smooth[agent_idx, :, 0][valid], smooth[agent_idx, :, 1][valid], color='orange')\n",
+    "    axs[0].set_xlabel(\"x\")\n",
+    "    axs[0].set_ylabel(\"y\")\n",
     "\n",
-    "- Unnormalized values\n",
-    "- Macroscopic (full dist)"
+    "    axs[1].set_title(f\"Global velocity (x, y); agent {agent_idx}\")\n",
+    "    axs[1].plot(raw[agent_idx, :, 2][valid], raw[agent_idx, :, 3][valid], color='b')\n",
+    "    axs[1].plot(smooth[agent_idx, :, 2][valid], smooth[agent_idx, :, 3][valid], color='orange')\n",
+    "    axs[1].set_xlabel(r\"$v_x$\")\n",
+    "    axs[1].set_ylabel(r\"$v_y$\")\n",
+    "\n",
+    "    axs[2].set_title(f\"Global heading; agent {agent_idx}\")\n",
+    "    axs[2].plot(list(range(valid.sum())), raw[agent_idx, :, 4][valid], color='b', label=\"raw\")\n",
+    "    axs[2].plot(list(range(valid.sum())), smooth[agent_idx, :, 4][valid], color='orange', label=\"smoothed\")\n",
+    "    axs[2].set_ylabel(r\"$\\theta$\")\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "\n",
+    "    fig.legend(loc=\"center right\", facecolor=\"white\", bbox_to_anchor=(1.15, 0.5))\n",
+    "    plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
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+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 1000x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in range(5):\n",
+    "    plot_trajectories(raw=ref_log_replay_raw, smooth=ref_log_replay_smooth, agent_idx=i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Aligning the scale of guidance data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Q1. Are the distributions from different modes (`vbd_online`, `log_replay` and `vbd_amortized`) on the same scale?\n",
+    "\n",
+    "- Unnormalized values\n",
+    "- Macroscopic (full dist)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Load npy array from the file\n",
     "FOLDER = \"examples/eval/figures_data/\"\n",
     "\n",
-    "ref_log_replay = np.load(f\"{FOLDER}reference_log_replay.npy\")\n",
-    "ref_vbd_amortized = np.load(f\"{FOLDER}reference_vbd_amortized.npy\")\n",
-    "ref_vbd_online = np.load(f\"{FOLDER}reference_vbd_online.npy\")"
+    "ref_log_replay = np.load(f\"{DATA_FOLDER}reference_log_replay.npy\")\n",
+    "ref_vbd_amortized = np.load(f\"{DATA_FOLDER}reference_vbd_amortized.npy\")\n",
+    "ref_vbd_online = np.load(f\"{DATA_FOLDER}reference_vbd_online.npy\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "((1180, 91, 6), (1180, 90, 6), (1180, 80, 6))"
+       "((194, 91, 6), (1180, 90, 6), (1180, 80, 6))"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/examples/eval/notebooks/obtain_guidance_data.py b/examples/eval/notebooks/obtain_guidance_data.py
index 1f67b0166..5184c42a7 100644
--- a/examples/eval/notebooks/obtain_guidance_data.py
+++ b/examples/eval/notebooks/obtain_guidance_data.py
@@ -11,7 +11,7 @@
 
 if __name__ == "__main__":
 
-    GUIDANCE_MODE = "vbd_online"
+    GUIDANCE_MODE = "log_replay"
     DATASET = "data/processed/wosac/validation_json_100"  # Ensure VBD trajectory structures are in here
     SAVE_PATH = "examples/eval/figures_data/"
 
@@ -24,12 +24,13 @@
         add_reference_speed=True,
         add_reference_pos_xy=True,
         init_mode="wosac_train",
+        smoothen_trajectory=True,
     )
     render_config = RenderConfig()
 
     train_loader = SceneDataLoader(
         root=DATASET,
-        batch_size=50,
+        batch_size=10,
         dataset_size=100,
         sample_with_replacement=False,
         shuffle=False,
@@ -59,5 +60,7 @@
         reference_traj[env.cont_agent_mask].detach().cpu().numpy()
     )
 
-    np.save(f"{SAVE_PATH}reference_{GUIDANCE_MODE}.npy", reference_traj_np)
+    np.save(
+        f"{SAVE_PATH}reference_{GUIDANCE_MODE}_smooth.npy", reference_traj_np
+    )
     print(f"Saved reference trajectory for {GUIDANCE_MODE} mode.")
diff --git a/gpudrive/env/config.py b/gpudrive/env/config.py
index b1368a440..36d9b7f64 100755
--- a/gpudrive/env/config.py
+++ b/gpudrive/env/config.py
@@ -41,6 +41,8 @@ class EnvConfig:
     add_reference_speed: bool = False  # speed time series
     add_reference_heading: bool = False  # heading time series
 
+    smoothen_trajectory: bool = False  # Filters out the trajectory
+
     # Maximum number of controlled agents in the scene
     max_controlled_agents: int = madrona_gpudrive.kMaxAgentCount
     num_worlds: int = 1  # Number of worlds in the environment
diff --git a/gpudrive/env/env_torch.py b/gpudrive/env/env_torch.py
index 8f21c16e6..6833a2ba8 100755
--- a/gpudrive/env/env_torch.py
+++ b/gpudrive/env/env_torch.py
@@ -35,6 +35,8 @@
 
 from gpudrive.integrations.vbd.data.utils import process_scenario_data
 
+from gpudrive.utils.preprocess import smooth_scenario
+
 
 class GPUDriveTorchEnv(GPUDriveGymEnv):
     """Torch Gym Environment that interfaces with the GPU Drive simulator."""
@@ -187,6 +189,12 @@ def setup_guidance(self):
         # Length of the guidance trajectory
         self.reference_traj_len = self.reference_trajectory.length
 
+        # Smooth the trajectory if specified
+        if self.config.smoothen_trajectory:
+            self.reference_trajectory = smooth_scenario(
+                self.reference_trajectory
+            )
+
     def _load_vbd_model(self, model_path):
         """
         Load the Versatile Behavior Diffusion (VBD) weights from checkpoint.
@@ -1478,19 +1486,20 @@ def get_scenario_ids(self):
 
     env_config = EnvConfig(
         guidance=True,
-        guidance_mode="vbd_online",  # Options: "log_replay", "vbd_amortized"
+        guidance_mode="log_replay",  # Options: "log_replay", "vbd_amortized"
         add_reference_pos_xy=True,
         add_reference_speed=True,
         add_reference_heading=True,
         reward_type="guided_autonomy",
         init_mode="wosac_train",
+        smoothen_trajectory=True,
     )
     render_config = RenderConfig()
 
     # Create data loader
     train_loader = SceneDataLoader(
         root="data/processed/wosac/debug",
-        batch_size=1,
+        batch_size=5,
         dataset_size=1,
         sample_with_replacement=False,
         shuffle=False,
diff --git a/gpudrive/utils/preprocess.py b/gpudrive/utils/preprocess.py
new file mode 100644
index 000000000..abc324988
--- /dev/null
+++ b/gpudrive/utils/preprocess.py
@@ -0,0 +1,136 @@
+import torch
+import numpy as np
+from tqdm import tqdm
+from typing import Dict, Tuple, List, Union
+from scipy.signal import savgol_filter
+from scipy.interpolate import interp1d
+
+from gpudrive.datatypes.trajectory import (
+    LogTrajectory,
+    VBDTrajectory,
+    VBDTrajectoryOnline,
+)
+
+
+def smooth_scenario(
+    reference_trajectory: Union[
+        LogTrajectory, VBDTrajectory, VBDTrajectoryOnline
+    ],
+    window_size: int = 11,
+    polyorder: int = 3,
+):
+    """
+    Smooths the trajectory of a scenario by applying filtering and interpolation techniques.
+
+    Args:
+        reference_trajectory: The trajectory to be smoothed. It can be of type LogTrajectory, VBDTrajectory, or VBDTrajectoryOnline.
+        The trajectory should contain the following attributes:
+            - vel_xy: A tensor of shape (num_worlds, num_agents, traj_len, 2) representing the velocity in x and y directions.
+            - yaw: A tensor of shape (num_worlds, num_agents, traj_len, 1) representing the yaw angle.
+            - valids: A tensor of shape (num_worlds, num_agents, traj_len, 1) representing the validity of each timestep.
+        window_size (int, optional): The size of the window used for smoothing. Defaults to 11.
+        polyorder (int, optional): The order of the polynomial used for smoothing. Defaults to 3.
+
+    Returns:
+        the updated reference_trajectory object.
+    """
+    original_valid = reference_trajectory.valids.bool().cpu().numpy()
+
+    num_worlds, num_agents, num_timesteps = (
+        original_valid.shape[0],
+        original_valid.shape[1],
+        original_valid.shape[2],
+    )
+
+    # Elements that we want to smooth
+    vel = np.concatenate(
+        [
+            reference_trajectory.vel_xy.cpu().numpy(),
+            np.sin(reference_trajectory.yaw.cpu().numpy()),
+            np.cos(reference_trajectory.yaw.cpu().numpy()),
+        ],
+        axis=-1,
+    )
+
+    smoothed_vel_xy = np.zeros_like(reference_trajectory.vel_xy)
+    smoothed_yaw = np.zeros_like(reference_trajectory.yaw)
+    smoothed_valid = np.zeros_like(original_valid, dtype=bool)
+
+    t = np.arange(num_timesteps)
+
+    for world_idx in tqdm(
+        range(num_worlds), colour="blue", desc="Smoothing guidance data"
+    ):
+        for agent_idx in range(num_agents):
+
+            # Extract raw data and valid mask
+            valid = original_valid[world_idx, agent_idx].squeeze(-1)
+
+            t_valid = t[valid]
+            vel_valid = vel[world_idx, agent_idx][valid]
+            valid_idx = np.where(valid)[0]
+
+            if valid.sum() <= window_size:
+                continue
+
+            # Use zscore to filter out outliers
+            std = np.clip(
+                np.std(vel_valid, axis=-2, keepdims=True),
+                a_min=0.1,
+                a_max=None,
+            )
+            mean = np.mean(vel_valid, axis=-2, keepdims=True)
+            z = np.abs((vel_valid - mean) / std)
+            filtered_idx = np.all(z < 4, axis=-1)
+            valid_idx = valid_idx[filtered_idx]
+
+            if len(valid_idx) == 0:  # skip if no valid data
+                continue
+
+            first_valid_idx = valid_idx[0]
+            last_valid_idx = valid_idx[-1]
+            if (last_valid_idx - first_valid_idx) <= 3:
+                continue
+
+            # Extract valid velocity data and interpolate
+            t_valid = t[valid_idx]
+
+            vel_valid = vel[world_idx, agent_idx][valid_idx, :]
+            vel_interp = interp1d(t_valid, vel_valid, axis=0, kind="linear")
+
+            t_interped = np.arange(first_valid_idx, last_valid_idx + 1)
+            vel_interped = vel_interp(t_interped)
+
+            # Smooth the interpolated data
+            vel_smoothed = savgol_filter(
+                vel_interped,
+                min(last_valid_idx - first_valid_idx, window_size),
+                polyorder,
+                axis=0,
+            )
+
+            # Store
+            smoothed_vel_xy[world_idx, agent_idx][
+                first_valid_idx : last_valid_idx + 1
+            ] = vel_smoothed[:, :2]
+            smoothed_yaw[world_idx, agent_idx][
+                first_valid_idx : last_valid_idx + 1
+            ] = np.expand_dims(
+                np.arctan2(vel_smoothed[:, 2], vel_smoothed[:, 3]), axis=-1
+            )
+            smoothed_valid[world_idx, agent_idx][
+                first_valid_idx : last_valid_idx + 1
+            ] = True
+
+    # Update the reference trajectory with smoothed velocity and yaw
+    reference_trajectory.vel_xy = torch.from_numpy(smoothed_vel_xy).to(
+        reference_trajectory.vel_xy.device
+    )
+    reference_trajectory.yaw = torch.from_numpy(smoothed_yaw).to(
+        reference_trajectory.yaw.device
+    )
+    reference_trajectory.valids = torch.from_numpy(smoothed_valid).to(
+        reference_trajectory.valids.device
+    )
+
+    return reference_trajectory

From 25f22d1461868ba8e4872c560ff4cafaef8247cd Mon Sep 17 00:00:00 2001
From: Daphne Cornelisse <33460159+daphne-cornelisse@users.noreply.github.com>
Date: Fri, 2 May 2025 15:27:54 -0400
Subject: [PATCH 07/38] Temporary fix: Access average z position (elevation)
 from log replays (#427)

* Intended usage for z-axis

* Access average z pos (elevation) from logs

* Fix intended usage of avg_z

* fixes

* z pos fixes

* Fix bug by converting all vals in tensor to floats

* remove print statement

* Cleanup

---------

Co-authored-by: kevin <kevinwinston184@gmail.com>
---
 examples/eval/run_wosac_eval.py | 25 +++++----
 gpudrive/datatypes/metadata.py  | 13 ++---
 gpudrive/env/env_torch.py       |  7 +++
 src/json_serialization.hpp      | 95 +++++++++++++++++++++++----------
 src/mgr.cpp                     | 17 +++---
 src/types.hpp                   | 25 ++++-----
 6 files changed, 116 insertions(+), 66 deletions(-)

diff --git a/examples/eval/run_wosac_eval.py b/examples/eval/run_wosac_eval.py
index 15101b32b..d243dad66 100644
--- a/examples/eval/run_wosac_eval.py
+++ b/examples/eval/run_wosac_eval.py
@@ -13,6 +13,7 @@
 from gpudrive.env.env_torch import GPUDriveTorchEnv
 from gpudrive.env.dataset import SceneDataLoader
 from gpudrive.datatypes.observation import GlobalEgoState
+from gpudrive.datatypes.metadata import Metadata
 from gpudrive.datatypes.info import Info
 from gpudrive.utils.checkpoint import load_agent
 from gpudrive.visualize.utils import img_from_fig
@@ -30,6 +31,11 @@
 
 def get_state(env):
     """Obtain raw agent states."""
+    avg_z_pos = Metadata.from_tensor(
+        metadata_tensor=env.sim.metadata_tensor(),
+        backend=env.backend,
+        device=env.device,
+    ).avg_z
     ego_state = GlobalEgoState.from_tensor(
         env.sim.absolute_self_observation_tensor(),
         backend=env.backend,
@@ -41,7 +47,7 @@ def get_state(env):
     return (
         ego_state.pos_x + mean_x,
         ego_state.pos_y + mean_y,
-        ego_state.pos_z,
+        avg_z_pos, #ego_state.pos_z
         ego_state.rotation_angle,
         ego_state.id,
     )
@@ -229,9 +235,9 @@ def rollout(
     # Settings
     MAX_AGENTS = 64
     NUM_ENVS = 1
-    DEVICE = "cuda"  # where to run the env rollouts
+    DEVICE = "cpu"  # where to run the env rollouts
     NUM_ROLLOUTS_PER_BATCH = 1
-    NUM_DATA_BATCHES = 1
+    NUM_DATA_BATCHES = 10
     INIT_STEPS = 10
     DATASET_SIZE = 100
     RENDER = False
@@ -251,7 +257,7 @@ def rollout(
 
     # Load agent
     agent = load_agent(
-        path_to_cpt="checkpoints/model_guidance_log_replay__S_100__04_29_19_49_30_053_015179.pt", 
+        path_to_cpt="checkpoints/model_guidance_log_replay__S_100__04_29_19_49_30_053_015179.pt",
     ).to(DEVICE)
 
     # Override default environment settings to match those the agent was trained with
@@ -265,17 +271,14 @@ def rollout(
     }
 
     # Add fixed overrides specific to WOSAC evaluation
-    fixed_overrides = {
-        "init_steps": INIT_STEPS,
-    }
-
+    config_dict["init_steps"] = INIT_STEPS
+    config_dict["init_mode"] = "wosac_eval"
+   
     logging.info(
         f"initializing env with init_mode = {config_dict['init_mode']}"
     )
 
-    env_config = dataclasses.replace(
-        default_config, **config_dict, **fixed_overrides
-    )
+    env_config = dataclasses.replace(default_config, **config_dict)
 
     # Make environment
     env = GPUDriveTorchEnv(
diff --git a/gpudrive/datatypes/metadata.py b/gpudrive/datatypes/metadata.py
index b0e6809e8..5b6b4a094 100644
--- a/gpudrive/datatypes/metadata.py
+++ b/gpudrive/datatypes/metadata.py
@@ -9,11 +9,11 @@ class Metadata:
     `MetaData` in src/types.hpp
     
     Attributes (all masks are 0/1 int of shape (NumWorlds, NumAgents)):
-        id: Unique ID of the agent (non-negative int).
-        isSdc: Whether agent is self-driving car (1/0, -1 padding). 
-        isOfInterest: IDs of agents interested (1/0, -1 padding).
-        isModeled: Whether agent's trajectory needs to be predicted for WOSAC (1/0, -1 padding).
+        is_sdc: Whether agent is self-driving car (1/0, -1 padding). 
+        objects_of_interest: IDs of agents interested (1/0, -1 padding).
+        tracks_to_predict: Whether agent's trajectory needs to be predicted for WOSAC (1/0, -1 padding).
         difficulty: Difficulty of the agent's trajectory to be predicted (0/1/2 if isModeled, 0 if !isModeled, -1 padding).
+        avg_z: Average z-coordinate (elevation) from the first 10 positions of the agent.
     """
 
     def __init__(self, metadata_tensor: torch.Tensor):
@@ -22,7 +22,8 @@ def __init__(self, metadata_tensor: torch.Tensor):
         self.objects_of_interest = metadata_tensor[:, :, 1]
         self.tracks_to_predict = metadata_tensor[:, :, 2]
         self.difficulty = metadata_tensor[:, :, 3]
-
+        self.avg_z = metadata_tensor[:, :, 4]
+        
     @classmethod
     def from_tensor(
         cls, metadata_tensor: madrona_gpudrive.madrona.Tensor, 
@@ -38,4 +39,4 @@ def from_tensor(
     @property
     def shape(self) -> tuple[int, ...]:
         """Shape (num_worlds, num_agents) of each metadata mask."""
-        return self.is_sdc.shape
\ No newline at end of file
+        return self.is_sdc.shape
diff --git a/gpudrive/env/env_torch.py b/gpudrive/env/env_torch.py
index 6833a2ba8..89ed38802 100755
--- a/gpudrive/env/env_torch.py
+++ b/gpudrive/env/env_torch.py
@@ -1518,6 +1518,13 @@ def get_scenario_ids(self):
 
     print(f"Number of controlled agents: {control_mask.sum()}")
 
+    metadata = Metadata.from_tensor(
+        env.sim.metadata_tensor(),
+        backend=env.backend,
+        device=env.device,
+    )
+    print(f"avg Z: {metadata.avg_z}")
+
     # Rollout
     obs = env.reset(mask=control_mask)
 
diff --git a/src/json_serialization.hpp b/src/json_serialization.hpp
index da69da5f1..20c61828a 100644
--- a/src/json_serialization.hpp
+++ b/src/json_serialization.hpp
@@ -15,17 +15,37 @@ namespace madrona_gpudrive
         p.y = j.at("y").get<float>();
     }
 
+    // Extract Z coordinate from position JSON
+    float extract_z_from_position(const nlohmann::json &j)
+    {
+        if (j.contains("z")) {
+            return j.at("z").get<float>();
+        }
+        return 0.0f; // Default z value if not provided
+    }
+
     void from_json(const nlohmann::json &j, MapObject &obj)
     {
         obj.mean = {0,0};
         uint32_t i = 0;
+
+        float zPositions[10];
+
         for (const auto &pos : j.at("position"))
-        { 
+        {
             if (i < MAX_POSITIONS)
             {
                 from_json(pos, obj.position[i]);
                 obj.mean.x += (obj.position[i].x - obj.mean.x)/(i+1);
                 obj.mean.y += (obj.position[i].y - obj.mean.y)/(i+1);
+
+                // Store z position separately
+                if (i < 10)
+                {
+                    float z = extract_z_from_position(pos);
+                    zPositions[i] = z;
+                }
+
                 ++i;
             }
             else
@@ -101,11 +121,28 @@ namespace madrona_gpudrive
             from_json(j.at("mark_as_expert"), obj.markAsExpert);
         }
 
-         // Initialize metadata fields to 0
-        obj.metadata.isSdc = 0;
-        obj.metadata.isObjectOfInterest = 0;
-        obj.metadata.isTrackToPredict = 0;
-        obj.metadata.difficulty = 0;
+        // Initialize metadata fields to 0
+        MetaData::zero(obj.metadata);
+
+        // Calculate average z position from the first 10 positions
+        // Store in the avgZ field of map object
+        float avgZ = 0.0f;
+        float avg_count = 0.0f;
+        for (int zi = 0; zi < 10; zi++) {
+            // Only consider valid z positions
+            if (obj.valid[zi]) {
+                avgZ += zPositions[zi];
+                avg_count += 1;
+            }
+        }
+        if (avg_count == 0) {
+            avgZ = 0.0f; // Avoid division by zero
+        }
+        else
+        {
+            avgZ /= avg_count; // Calculate average
+        }
+        obj.metadata.avgZ = avgZ;
 
         // Initialize VBD trajectories to zeros
         for (int i = 0; i < consts::episodeLen; i++) {
@@ -151,7 +188,7 @@ namespace madrona_gpudrive
         else
             road.type = EntityType::None;
 
-        
+
         std::vector<MapVector2> geometry_points_;
         for(const auto &point: j.at("geometry"))
         {
@@ -232,7 +269,7 @@ namespace madrona_gpudrive
             {
                 if(i==MAX_GEOMETRY)
                     break;
-                road.geometry[i] = geometry_points_[i * sample_every_n_]; 
+                road.geometry[i] = geometry_points_[i * sample_every_n_];
             }
             road.numPoints = num_sampled_points;
         }
@@ -309,14 +346,14 @@ namespace madrona_gpudrive
 
         std::string scenario_id = j.at("scenario_id").get<std::string>();
         std::strncpy(map.scenarioId, scenario_id.c_str(), sizeof(map.scenarioId));
-        
+
         auto mean = calc_mean(j);
         map.mean = {mean.first, mean.second};
         map.numObjects = std::min(j.at("objects").size(), static_cast<size_t>(MAX_OBJECTS));
 
         const auto& metadata = j.at("metadata");
         int sdc_index = metadata.at("sdc_track_index").get<int>();
-        
+
         // Create id to object index mapping
         std::unordered_map<int, size_t> idToObjIdx;
         size_t idx = 0;
@@ -343,27 +380,27 @@ namespace madrona_gpudrive
         // Initialize SDC first if valid
         if (sdc_index >= 0 && sdc_index < j.at("objects").size()) {
             j.at("objects")[sdc_index].get_to(map.objects[0]);
-            map.objects[0].metadata.isSdc = 1;
-            
+            map.objects[0].metadata.isSdc = 1.0f;
+
             // Set additional metadata if needed
             int sdc_id = map.objects[0].id;
             if (tracks_to_predict_indices.find(sdc_index) != tracks_to_predict_indices.end()) {
-                map.objects[0].metadata.isTrackToPredict = 1;
+                map.objects[0].metadata.isTrackToPredict = 1.0f;
                 // Find and set difficulty
                 for (const auto& track : metadata.at("tracks_to_predict")) {
                     if (track.at("track_index").get<int>() == sdc_index) {
-                        map.objects[0].metadata.difficulty = track.at("difficulty").get<int>();
+                        map.objects[0].metadata.difficulty = static_cast<float>(track.at("difficulty").get<int>());
                         break;
                     }
                 }
             }
             if (objects_of_interest_ids.find(sdc_id) != objects_of_interest_ids.end()) {
-                map.objects[0].metadata.isObjectOfInterest = 1;
+                map.objects[0].metadata.isObjectOfInterest = 1.0f;
             }
-            
+
             idToObjIdx[sdc_id] = 0;
             idx = 1;
-            
+
             // Remove SDC from sets to avoid double processing
             tracks_to_predict_indices.erase(sdc_index);
             objects_of_interest_ids.erase(sdc_id);
@@ -372,25 +409,25 @@ namespace madrona_gpudrive
         // Initialize tracks_to_predict objects (excluding SDC)
         for (size_t i = 0; i < j.at("objects").size() && idx < map.numObjects; i++) {
             if (i == sdc_index) continue; // Skip SDC as it's already initialized
-            
+
             if (tracks_to_predict_indices.find(i) != tracks_to_predict_indices.end()) {
                 j.at("objects")[i].get_to(map.objects[idx]);
-                map.objects[idx].metadata.isTrackToPredict = 1;
-                
+                map.objects[idx].metadata.isTrackToPredict = 1.0f;
+
                 // Find and set difficulty
                 for (const auto& track : metadata.at("tracks_to_predict")) {
                     if (track.at("track_index").get<int>() == static_cast<int>(i)) {
-                        map.objects[idx].metadata.difficulty = track.at("difficulty").get<int>();
+                        map.objects[idx].metadata.difficulty = static_cast<float>(track.at("difficulty").get<int>());
                         break;
                     }
                 }
-                
+
                 // Check if also object of interest
                 if (objects_of_interest_ids.find(map.objects[idx].id) != objects_of_interest_ids.end()) {
-                    map.objects[idx].metadata.isObjectOfInterest = 1;
+                    map.objects[idx].metadata.isObjectOfInterest = 1.0f;
                     objects_of_interest_ids.erase(map.objects[idx].id);
                 }
-                
+
                 idToObjIdx[map.objects[idx].id] = idx;
                 idx++;
             }
@@ -399,12 +436,12 @@ namespace madrona_gpudrive
         // Initialize objects_of_interest (excluding those already processed)
         for (size_t i = 0; i < j.at("objects").size() && idx < map.numObjects; i++) {
             if (i == sdc_index) continue;
-            
+
             int obj_id = j.at("objects")[i].at("id").get<int>();
             if (objects_of_interest_ids.find(obj_id) != objects_of_interest_ids.end()) {
                 j.at("objects")[i].get_to(map.objects[idx]);
-                map.objects[idx].metadata.isObjectOfInterest = 1;
-                
+                map.objects[idx].metadata.isObjectOfInterest = 1.0f;
+
                 idToObjIdx[map.objects[idx].id] = idx;
                 idx++;
             }
@@ -413,7 +450,7 @@ namespace madrona_gpudrive
         // Initialize all remaining objects
         for (size_t i = 0; i < j.at("objects").size() && idx < map.numObjects; i++) {
             if (i == sdc_index) continue;
-            
+
             int obj_id = j.at("objects")[i].at("id").get<int>();
             if (idToObjIdx.find(obj_id) == idToObjIdx.end()) { // Check if not already processed
                 j.at("objects")[i].get_to(map.objects[idx]);
@@ -421,7 +458,7 @@ namespace madrona_gpudrive
                 idx++;
             }
         }
-        
+
         // Process roads
         map.numRoads = std::min(j.at("roads").size(), static_cast<size_t>(MAX_ROADS));
         size_t countRoadPoints = 0;
diff --git a/src/mgr.cpp b/src/mgr.cpp
index 2fa511897..092cb184a 100755
--- a/src/mgr.cpp
+++ b/src/mgr.cpp
@@ -896,19 +896,20 @@ Tensor Manager::scenarioIdTensor() const {
 
 Tensor Manager::metadataTensor() const {
     return impl_->exportTensor(
-        ExportID::MetaData, TensorElementType::Int32,
+        ExportID::MetaData, TensorElementType::Float32,
         {impl_->numWorlds, consts::kMaxAgentCount, MetaDataExportSize}
     );
 }
 
 Tensor Manager::vbdTrajectoryTensor() const {
-    return impl_->exportTensor(ExportID::VBDTrajectory, TensorElementType::Float32,
-                               {
-                                   impl_->numWorlds,
-                                   consts::kMaxAgentCount,
-                                   consts::episodeLen,
-                                   5,
-                               });
+    return impl_->exportTensor(
+        ExportID::VBDTrajectory, TensorElementType::Float32,
+    {
+        impl_->numWorlds,
+        consts::kMaxAgentCount,
+        consts::episodeLen,
+        5,
+    });
 }
 
 void Manager::triggerReset(int32_t world_idx)
diff --git a/src/types.hpp b/src/types.hpp
index a75d801ef..8d9fd25d8 100755
--- a/src/types.hpp
+++ b/src/types.hpp
@@ -435,24 +435,25 @@ namespace madrona_gpudrive
     const size_t ScenarioIdExportSize = 32;
     static_assert(sizeof(ScenarioId) == sizeof(char32_t) * ScenarioIdExportSize);
 
-    //Metadata struct : using agent IDs.
     struct MetaData
     {
-        int32_t isSdc;
-        int32_t isObjectOfInterest;
-        int32_t isTrackToPredict;
-        int32_t difficulty;
-
+        float isSdc;                
+        float isObjectOfInterest;   
+        float isTrackToPredict;     
+        float difficulty;           
+        float avgZ;
+    
         static inline void zero(MetaData& metadata)
         {
-            metadata.isSdc = -1;
-            metadata.isObjectOfInterest = -1;
-            metadata.isTrackToPredict = -1;
-            metadata.difficulty = -1;
+            metadata.isSdc = -1.0f;           
+            metadata.isObjectOfInterest = -1.0f; 
+            metadata.isTrackToPredict = -1.0f;   
+            metadata.difficulty = -1.0f;         
+            metadata.avgZ = 0.0f;
         }
     };
-    const size_t MetaDataExportSize = 4;
-    static_assert(sizeof(MetaData) == sizeof(int32_t) * MetaDataExportSize);
+    const size_t MetaDataExportSize = 5;
+    static_assert(sizeof(MetaData) == sizeof(int32_t) * (MetaDataExportSize - 1) + sizeof(float));
 
     struct AgentInterface : public madrona::Archetype<
                                 Action,

From 1777d0c93e367f844be4412782204304a0b727bf Mon Sep 17 00:00:00 2001
From: Daphne Cornelisse <33460159+daphne-cornelisse@users.noreply.github.com>
Date: Thu, 8 May 2025 09:39:47 -0400
Subject: [PATCH 08/38] Res/scaling (#436)

* Small improvements

* Minor training improvements

* Rebase over dev

* Bookkeeping

* Bookkeeping

* Small fixes for gpu

* wip: config

* Make adding the action optional

* wip

* Analysis notebooks

* WIP: new reward mode

* Frequency

* Make sure to normalize rewards and other changes

* Minor

* Update model

* Add bonus at end of episode mechanism

* Fixes

* Improve renderer

* Rmv one-hot encoding

* Fix render

* Adding back the whole shazam

* Naming stuff

* Add data folder as cmdline arg

* Add data folder as cmdline arg

* Minor bug fixes

* Cleanup and better error message

* Align reward components scale
---
 baselines/ppo/config/ppo_guided_autonomy.yaml |    33 +-
 baselines/ppo/ppo_guided_autonomy.py          |    28 +-
 ...eplay__S_100__04_29_19_49_30_053_015179.pt |   Bin 1493922 -> 0 bytes
 ...rogress__S_1__05_04_17_37_18_741_001677.pt |     1 +
 examples/eval/README.md                       |     9 +
 examples/eval/__init__.py                     |     0
 ...c_results.ipynb => 01_wosac_results.ipynb} |     0
 ....ipynb => 02_guidance_data_analysis.ipynb} |     0
 .../notebooks/03_qualitative_samples.ipynb    | 11515 ++++++++++++++++
 examples/eval/rollout_test.py                 |   129 +
 examples/eval/run_wosac_eval.py               |    34 +-
 gpudrive/env/config.py                        |    15 +-
 gpudrive/env/constants.py                     |     2 +-
 gpudrive/env/env_puffer.py                    |   311 +-
 gpudrive/env/env_torch.py                     |   533 +-
 gpudrive/integrations/puffer/ppo.py           |     6 +-
 gpudrive/networks/agents.py                   |     1 -
 gpudrive/utils/generate_sbatch.py             |     9 +-
 gpudrive/utils/preprocess.py                  |     4 +-
 gpudrive/visualize/core.py                    |    86 +-
 20 files changed, 12325 insertions(+), 391 deletions(-)
 delete mode 100644 checkpoints/model_guidance_log_replay__S_100__04_29_19_49_30_053_015179.pt
 create mode 120000 checkpoints/model_guidance_progress__S_1__05_04_17_37_18_741_001677.pt
 delete mode 100644 examples/eval/__init__.py
 rename examples/eval/notebooks/{wosac_results.ipynb => 01_wosac_results.ipynb} (100%)
 rename examples/eval/notebooks/{guidance_data_analysis.ipynb => 02_guidance_data_analysis.ipynb} (100%)
 create mode 100644 examples/eval/notebooks/03_qualitative_samples.ipynb
 create mode 100644 examples/eval/rollout_test.py

diff --git a/baselines/ppo/config/ppo_guided_autonomy.yaml b/baselines/ppo/config/ppo_guided_autonomy.yaml
index 4ebe7f681..02282cebd 100644
--- a/baselines/ppo/config/ppo_guided_autonomy.yaml
+++ b/baselines/ppo/config/ppo_guided_autonomy.yaml
@@ -2,14 +2,14 @@ mode: "train"
 use_rnn: false
 eval_model_path: null
 baseline: false
-data_dir: data/processed/wosac/validation_json_100
+data_dir: data/processed/wosac/validation_json_1
 continue_training: false
 model_cpt: null
 
 environment: # Overrides default environment configs (see pygpudrive/env/config.py)
   name: "gpudrive"
   num_worlds: 100 # Number of parallel environments
-  k_unique_scenes: 100 # Number of unique scenes to sample from
+  k_unique_scenes: 1 # Number of unique scenes to sample from
   max_controlled_agents: 64 # Maximum number of agents controlled by the model. Make sure this aligns with the variable kMaxAgentCount in src/consts.hpp
   ego_state: true
   road_map_obs: true
@@ -23,16 +23,15 @@ environment: # Overrides default environment configs (see pygpudrive/env/config.
   add_reference_pos_xy: true # If true, a reference path is added to the ego observation
   add_reference_speed: true # If true, the reference speeds are added to the ego observation
   add_reference_heading: true # If true, the reference heading are added to the ego observation
-  prob_reference_dropout: 0.0 # Value between 0 and 1, probability of a reference point to be zeroed out
+  smoothen_trajectory: true # If true, the velocities and headings are smoothed
 
   # Reward function
   reward_type: "guided_autonomy"
-  collision_weight: -0.01
+  collision_weight: -0.01 
   off_road_weight: -0.01
-  guidance_pos_xy_weight: 0.01
   guidance_speed_weight: 0.01
   guidance_heading_weight: 0.01
-  smoothness_weight: 0.001
+  smoothness_weight: 0.0001
 
   init_mode: wosac_train
   dynamics_model: "classic"
@@ -52,12 +51,12 @@ environment: # Overrides default environment configs (see pygpudrive/env/config.
 wandb:
   entity: ""
   project: "humanlike"
-  group: "wosac_scale_100_base"
+  group: "wosac_1"
   mode: "online" # Options: online, offline, disabled
   tags: ["ppo", "ff"]
 
 train:
-  exp_id: guidance_log_replay # Set dynamically in the script if needed
+  exp_id: guidance_logs # Set dynamically in the script if needed
   seed: 42
   cpu_offload: false
   device: "cuda" # Dynamically set to cuda if available, else cpu
@@ -67,7 +66,7 @@ train:
 
   # # # Data sampling # # #
   resample_scenes: false
-  resample_dataset_size: 500 # Number of unique scenes to sample from
+  resample_dataset_size: 10_000 # Number of unique scenes to sample from
   resample_interval: 2_000_000
   sample_with_replacement: false
   shuffle_dataset: true
@@ -80,22 +79,22 @@ train:
   minibatch_size: 8192
   learning_rate: 3e-4
   anneal_lr: true
-  gamma: 0.99
+  gamma: 1.0
   gae_lambda: 0.95
   update_epochs: 4
   norm_adv: true
   clip_coef: 0.2
   clip_vloss: false
   vf_clip_coef: 0.2
-  ent_coef: 0.005
+  ent_coef: 0.003
   vf_coef: 0.5
   max_grad_norm: 0.5
   target_kl: null
 
   # # # Logging # # #
-  log_window: 500
+  log_window: 100
   track_realism_metrics: true # Log human-like metrics
-  track_n_worlds: 3 # Number of worlds to track
+  track_n_worlds: 2 # Number of worlds to track
 
   # # # Network # # #
   network:
@@ -109,12 +108,14 @@ train:
   checkpoint_path: "./runs"
 
   # # # Rendering # # #
-  render: false # Determines whether to render the environment (note: will slow down training)
+  render: true # Determines whether to render the environment (note: will slow down training)
   render_3d: false # Render simulator state in 3d or 2d
-  render_interval: 300 # Render every k iterations
+  render_interval: 500 # Render every k iterations
+  render_every_t: 10 # Render every k timesteps
   render_k_scenarios: 1 # Number of scenarios to render
+  render_agent_idx: [0] # Agent observations to render
   render_format: "mp4" # Options: gif, mp4
-  render_fps: 20 # Frames per second
+  render_fps: 5 # Frames per second
   zoom_radius: 100
   plot_guidance_pos_xy: true
 
diff --git a/baselines/ppo/ppo_guided_autonomy.py b/baselines/ppo/ppo_guided_autonomy.py
index 6d3ed5b3d..53bdde822 100644
--- a/baselines/ppo/ppo_guided_autonomy.py
+++ b/baselines/ppo/ppo_guided_autonomy.py
@@ -109,15 +109,22 @@ def run(
     ] = "baselines/ppo/config/ppo_guided_autonomy.yaml",
     *,
     # fmt: off
+    # Dataset options
+    data_dir: Annotated[Optional[str], typer.Option(help="The path to the dataset")] = None,
     # Environment options
     num_worlds: Annotated[Optional[int], typer.Option(help="Number of parallel envs")] = None,
     max_controlled_agents: Annotated[Optional[int], typer.Option(help="Number of controlled agents")] = None,
     k_unique_scenes: Annotated[Optional[int], typer.Option(help="The number of unique scenes to sample")] = None,
     collision_weight: Annotated[Optional[float], typer.Option(help="The weight for collision penalty")] = None,
     off_road_weight: Annotated[Optional[float], typer.Option(help="The weight for off-road penalty")] = None,
-    guidance_pos_xy_weight: Annotated[Optional[float], typer.Option(help="Scale for realism rewards")] = None,
     guidance_speed_weight: Annotated[Optional[float], typer.Option(help="Scale for realism rewards")] = None,
     guidance_heading_weight: Annotated[Optional[float], typer.Option(help="Scale for realism rewards")] = None,
+    add_reference_pos_xy: Annotated[Optional[int], typer.Option(help="0 or 1")] = None,
+    add_reference_speed: Annotated[Optional[int], typer.Option(help="0 or 1")] = None,
+    add_reference_heading: Annotated[Optional[int], typer.Option(help="0 or 1")] = None,
+    add_previous_action: Annotated[Optional[int], typer.Option(help="0 or 1")] = None,
+    smoothen_trajectory: Annotated[Optional[int], typer.Option(help="0 or 1")] = None,
+
     smoothness_weight: Annotated[Optional[float], typer.Option(help="Scale for realism rewards")] = None,
     dist_to_goal_threshold: Annotated[Optional[float], typer.Option(help="The distance threshold for goal-achieved")] = None,
     randomize_rewards: Annotated[Optional[int], typer.Option(help="If reward_type == reward_conditioned, choose the condition_mode; 0 or 1")] = 0,
@@ -154,6 +161,9 @@ def run(
     # Load default configs
     config = load_config(config_path)
 
+    if data_dir is not None:
+        config.data_dir = data_dir
+
     if config.environment.reward_type == "reward_conditioned":
         if bool(randomize_rewards):
             config.environment.condition_mode = "random"
@@ -171,7 +181,6 @@ def run(
         "k_unique_scenes": k_unique_scenes,
         "collision_weight": collision_weight,
         "off_road_weight": off_road_weight,
-        "guidance_pos_xy_weight": guidance_pos_xy_weight,
         "smoothness_weight": smoothness_weight,
         "guidance_speed_weight": guidance_speed_weight,
         "guidance_heading_weight": guidance_heading_weight,
@@ -184,6 +193,21 @@ def run(
         else bool(remove_non_vehicles),
         "vbd_model_path": vbd_model_path,
         "init_steps": init_steps,
+        "add_previous_action": None
+        if add_previous_action is None
+        else bool(add_previous_action),
+        "add_reference_pos_xy": None
+        if add_reference_pos_xy is None
+        else bool(add_reference_pos_xy),
+        "add_reference_speed": None
+        if add_reference_speed is None
+        else bool(add_reference_speed),
+        "add_reference_heading": None
+        if add_reference_heading is None
+        else bool(add_reference_heading),
+        "smoothen_trajectory": None
+        if smoothen_trajectory is None
+        else bool(smoothen_trajectory),
     }
     config.environment.update(
         {k: v for k, v in env_config.items() if v is not None}
diff --git a/checkpoints/model_guidance_log_replay__S_100__04_29_19_49_30_053_015179.pt b/checkpoints/model_guidance_log_replay__S_100__04_29_19_49_30_053_015179.pt
deleted file mode 100644
index 46977fce02edcf58abc0f1844bb2e6aa100467c9..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/checkpoints/model_guidance_progress__S_1__05_04_17_37_18_741_001677.pt b/checkpoints/model_guidance_progress__S_1__05_04_17_37_18_741_001677.pt
new file mode 120000
index 000000000..e6ab06eb1
--- /dev/null
+++ b/checkpoints/model_guidance_progress__S_1__05_04_17_37_18_741_001677.pt
@@ -0,0 +1 @@
+/home/emerge/gpudrive/runs/guidance_progress__S_1__05_04_17_37_18_741/model_guidance_progress__S_1__05_04_17_37_18_741_001677.pt
\ No newline at end of file
diff --git a/examples/eval/README.md b/examples/eval/README.md
index 407f53789..e8558304f 100644
--- a/examples/eval/README.md
+++ b/examples/eval/README.md
@@ -12,6 +12,15 @@ Requirement to process the data
 pip install --no-deps git+https://github.com/waymo-research/waymax.git@main#egg=waymo-waymax
 ```
 
+You also need
+```
+pip install tensorflow==2.13.0
+pip install scikit-learn
+pip install tensorflow-probability==0.21.0
+pip install --upgrade typing-extensions
+```
+
+
 ## Dataset
 Extract TF example from raw waymo TF example dataset using
 ```
diff --git a/examples/eval/__init__.py b/examples/eval/__init__.py
deleted file mode 100644
index e69de29bb..000000000
diff --git a/examples/eval/notebooks/wosac_results.ipynb b/examples/eval/notebooks/01_wosac_results.ipynb
similarity index 100%
rename from examples/eval/notebooks/wosac_results.ipynb
rename to examples/eval/notebooks/01_wosac_results.ipynb
diff --git a/examples/eval/notebooks/guidance_data_analysis.ipynb b/examples/eval/notebooks/02_guidance_data_analysis.ipynb
similarity index 100%
rename from examples/eval/notebooks/guidance_data_analysis.ipynb
rename to examples/eval/notebooks/02_guidance_data_analysis.ipynb
diff --git a/examples/eval/notebooks/03_qualitative_samples.ipynb b/examples/eval/notebooks/03_qualitative_samples.ipynb
new file mode 100644
index 000000000..d2489dd5a
--- /dev/null
+++ b/examples/eval/notebooks/03_qualitative_samples.ipynb
@@ -0,0 +1,11515 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "104c55cd",
+   "metadata": {},
+   "source": [
+    "### Qualitative analysis\n",
+    "\n",
+    "- What kind of trajectories does VBD generate?\n",
+    "- Can an RL agent correct for unrealistic trajectories?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "992178d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import importlib\n",
+    "import gpudrive\n",
+    "importlib.reload(gpudrive)\n",
+    "\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import torch\n",
+    "import mediapy\n",
+    "import dataclasses\n",
+    "from pathlib import Path\n",
+    "from tqdm import tqdm\n",
+    "# Set working directory to the base directory 'gpudrive_madrona'\n",
+    "working_dir = Path.cwd()\n",
+    "while working_dir.name != 'gpudrive':\n",
+    "    working_dir = working_dir.parent\n",
+    "    if working_dir == Path.home():\n",
+    "        raise FileNotFoundError(\"Base directory 'gpudrive_madrona' not found\")\n",
+    "os.chdir(working_dir)\n",
+    "import torch\n",
+    "from PIL import Image\n",
+    "import seaborn as sns\n",
+    "import warnings\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import datetime\n",
+    "import madrona_gpudrive\n",
+    "\n",
+    "from gpudrive.env.env_torch import GPUDriveTorchEnv\n",
+    "from gpudrive.env.config import EnvConfig, RenderConfig\n",
+    "from gpudrive.env.dataset import SceneDataLoader\n",
+    "from gpudrive.datatypes.observation import GlobalEgoState\n",
+    "from gpudrive.datatypes.info import Info\n",
+    "\n",
+    "from gpudrive.visualize.utils import img_from_fig\n",
+    "from gpudrive.utils.checkpoint import load_agent\n",
+    "\n",
+    "sns.set(\"notebook\", font_scale=1.05, rc={\"figure.figsize\": (10, 5)})\n",
+    "sns.set_style(\"ticks\", rc={\"figure.facecolor\": \"none\", \"axes.facecolor\": \"none\"})\n",
+    "%config InlineBackend.figure_format = 'svg'\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "plt.set_loglevel(\"WARNING\")\n",
+    "mpl.rcParams[\"lines.markersize\"] = 8\n",
+    "\n",
+    "plt.set_loglevel(\"WARNING\")\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60eb9f03",
+   "metadata": {},
+   "source": [
+    "### Helper functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03617bca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rollout(\n",
+    "    env: GPUDriveTorchEnv,\n",
+    "    sim_agent: torch.nn.Module,\n",
+    "    init_steps: int,\n",
+    "    num_envs: int,\n",
+    "    max_agents: int,\n",
+    "    device: str,\n",
+    "    render_simulator_states: bool = False,\n",
+    "    render_agent_pov: bool = False,\n",
+    "    render_every_n_steps: int = 5,\n",
+    "    save_videos: bool = True,\n",
+    "    video_dir: str = \"videos\",\n",
+    "    video_format: str = \"gif\",\n",
+    "):\n",
+    "    \"\"\"Rollout agent in the environment and return the scenario rollouts.\"\"\"\n",
+    "\n",
+    "    if save_videos:\n",
+    "        os.makedirs(video_dir, exist_ok=True)\n",
+    "\n",
+    "    # Storage\n",
+    "    env_ids = list(range(num_envs))\n",
+    "    simulator_state_frames = {env_id: [] for env_id in range(num_envs)}\n",
+    "    agent_observation_frames = {env_id: [] for env_id in range(num_envs)}\n",
+    "\n",
+    "    _ = env.reset()\n",
+    "\n",
+    "    # Zero out actions for parked vehicles\n",
+    "    info = Info.from_tensor(\n",
+    "        env.sim.info_tensor(),\n",
+    "        backend=env.backend,\n",
+    "        device=env.device,\n",
+    "    )\n",
+    "    control_mask_all = env.cont_agent_mask.clone()\n",
+    "    zero_action_mask = (info.off_road == 1) | (\n",
+    "        info.collided_with_vehicle == 1\n",
+    "    ) & (info.type == int(madrona_gpudrive.EntityType.Vehicle))\n",
+    "    control_mask = control_mask_all & ~zero_action_mask\n",
+    "\n",
+    "    next_obs = env.reset(control_mask)\n",
+    "\n",
+    "    # Get scenario ids\n",
+    "    scenario_ids_dict = env.get_scenario_ids()\n",
+    "    scenario_ids = list(scenario_ids_dict.values())\n",
+    "\n",
+    "    pos_x_list = []\n",
+    "    pos_y_list = []\n",
+    "    heading_list = []\n",
+    "    done_list = [env.get_dones()]\n",
+    "\n",
+    "    for time_step in range(env.episode_len - init_steps):\n",
+    "\n",
+    "        # Predict actions\n",
+    "        action, _, _, _ = sim_agent(next_obs)\n",
+    "\n",
+    "        action_template = torch.zeros(\n",
+    "            (num_envs, max_agents), dtype=torch.int64, device=device\n",
+    "        )\n",
+    "        action_template[control_mask] = action.to(device)\n",
+    "\n",
+    "        # Step\n",
+    "        env.step_dynamics(action_template)\n",
+    "\n",
+    "        # Render\n",
+    "        if render_simulator_states and time_step % render_every_n_steps == 0:\n",
+    "            sim_states = env.vis.plot_simulator_state(\n",
+    "                env_indices=env_ids,\n",
+    "                zoom_radius=100,\n",
+    "                time_steps=[time_step] * len(env_ids),\n",
+    "                plot_guidance_pos_xy=True,\n",
+    "            )\n",
+    "            for idx in range(num_envs):\n",
+    "                simulator_state_frames[idx].append(\n",
+    "                    img_from_fig(sim_states[idx])\n",
+    "                )\n",
+    "\n",
+    "        if render_agent_pov and time_step % render_every_n_steps == 0:\n",
+    "            agent_obs = env.vis.plot_agent_observation(\n",
+    "                env_idx=0,\n",
+    "                agent_idx=0,\n",
+    "                figsize=(10, 10),\n",
+    "                trajectory=env.reference_path[0, :, :].to(\"cpu\"),\n",
+    "            )\n",
+    "            agent_observation_frames[idx].append(img_from_fig(agent_obs))\n",
+    "\n",
+    "        # Get next observation\n",
+    "        next_obs = env.get_obs(control_mask)\n",
+    "        done = env.get_dones()\n",
+    "        \n",
+    "        states = GlobalEgoState.from_tensor(\n",
+    "            env.sim.absolute_self_observation_tensor(),\n",
+    "            backend=env.backend,\n",
+    "            device=env.device,\n",
+    "        )\n",
+    "        \n",
+    "        pos_xy = states.pos_xy\n",
+    "        heading = states.rotation_angle\n",
+    "        vel_xy = states.vel_xy\n",
+    "        \n",
+    "        pos_x_list.append(pos_xy[:, 0])\n",
+    "        pos_y_list.append(pos_xy[:, 1])\n",
+    "        heading_list.append(heading)\n",
+    "        done_list.append(done)\n",
+    "    _ = done_list.pop()\n",
+    "\n",
+    "    if save_videos:\n",
+    "        for idx in range(num_envs):\n",
+    "            scenario_id = scenario_ids_dict[idx]\n",
+    "            if (\n",
+    "                render_simulator_states\n",
+    "                and len(simulator_state_frames[idx]) > 0\n",
+    "            ):\n",
+    "                mediapy.write_video(\n",
+    "                    f\"{video_dir}/sim_state_env_{idx}_{scenario_id}.{video_format}\",\n",
+    "                    np.array(simulator_state_frames[idx]),\n",
+    "                    fps=8,\n",
+    "                    codec=video_format,\n",
+    "                )\n",
+    "\n",
+    "        if render_agent_pov and len(agent_observation_frames[0]) > 0:\n",
+    "            scenario_id = scenario_ids_dict[0]\n",
+    "            mediapy.write_video(\n",
+    "                f\"{video_dir}/agent_0_{scenario_id}.{video_format}\",\n",
+    "                np.array(agent_observation_frames[0]),\n",
+    "                fps=8,\n",
+    "                codec=video_format,\n",
+    "            )\n",
+    "\n",
+    "    # Generate Scenario\n",
+    "    pos_x_stack = torch.stack(pos_x_list, dim=-1).cpu().numpy()\n",
+    "    pos_y_stack = torch.stack(pos_y_list, dim=-1).cpu().numpy()\n",
+    "    heading_stack = torch.stack(heading_list, dim=-1).cpu().numpy()\n",
+    "    done_stack = torch.stack(done_list, dim=-1).cpu().numpy()\n",
+    "    control_mask = control_mask.cpu().numpy()\n",
+    "    \n",
+    "    return pos_x_stack, pos_y_stack, heading_stack, done_stack, control_mask"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8f13883",
+   "metadata": {},
+   "source": [
+    "### Settings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "885c023a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_FOLDER = \"data/processed/wosac/validation_json_3\"\n",
+    "FIGURES_FOLDER = \"examples/eval/figures\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd75f099",
+   "metadata": {},
+   "source": [
+    "### Make environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d5b19c53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Settings\n",
+    "MAX_AGENTS = 64\n",
+    "NUM_ENVS = 3\n",
+    "DEVICE = \"cpu\"  \n",
+    "NUM_ROLLOUTS = 1\n",
+    "INIT_STEPS = 0\n",
+    "DATASET_SIZE = 100\n",
+    "RENDER = False\n",
+    "\n",
+    "# Create data loader\n",
+    "val_loader = SceneDataLoader(\n",
+    "    root=DATA_FOLDER,\n",
+    "    batch_size=NUM_ENVS,\n",
+    "    dataset_size=DATASET_SIZE,\n",
+    "    sample_with_replacement=False,\n",
+    "    shuffle=False,\n",
+    "    file_prefix=\"\",\n",
+    ")\n",
+    "\n",
+    "# Load agent\n",
+    "agent = load_agent(\n",
+    "    path_to_cpt=\"checkpoints/model_guidance_log_replay__S_3__05_02_18_49_49_637_000500.pt\",\n",
+    ").to(DEVICE)\n",
+    "\n",
+    "# Override default environment settings to match those the agent was trained with\n",
+    "default_config = EnvConfig()\n",
+    "config_dict = {\n",
+    "    field.name: getattr(agent.config, field.name)\n",
+    "    for field in dataclasses.fields(EnvConfig)\n",
+    "    if hasattr(agent.config, field.name)\n",
+    "    and getattr(agent.config, field.name)\n",
+    "    != getattr(default_config, field.name)\n",
+    "}\n",
+    "\n",
+    "env_config = dataclasses.replace(default_config, **config_dict)\n",
+    "\n",
+    "# Make environment\n",
+    "env = GPUDriveTorchEnv(\n",
+    "    config=env_config,\n",
+    "    data_loader=val_loader,\n",
+    "    max_cont_agents=MAX_AGENTS,\n",
+    "    device=DEVICE,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44ccb33f",
+   "metadata": {},
+   "source": [
+    "### Rollouts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(\n",
+    "    pos_x_stack, \n",
+    "    pos_y_stack, \n",
+    "    heading_stack, \n",
+    "    done_stack, \n",
+    "    control_mask\n",
+    ") = rollout(\n",
+    "    env=env,\n",
+    "    sim_agent=agent,\n",
+    "    init_steps=INIT_STEPS,\n",
+    "    num_envs=NUM_ENVS,\n",
+    "    max_agents=MAX_AGENTS,\n",
+    "    device=DEVICE,\n",
+    "    render_simulator_states=RENDER,\n",
+    "    render_agent_pov=RENDER,\n",
+    "    save_videos=RENDER,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4797e488",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 2, 90)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pos_x_stack.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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+       " <metadata>\n",
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+       "   <cc:Work>\n",
+       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
+       "    <dc:date>2025-05-02T20:16:58.512575</dc:date>\n",
+       "    <dc:format>image/svg+xml</dc:format>\n",
+       "    <dc:creator>\n",
+       "     <cc:Agent>\n",
+       "      <dc:title>Matplotlib v3.9.0, https://matplotlib.org/</dc:title>\n",
+       "     </cc:Agent>\n",
+       "    </dc:creator>\n",
+       "   </cc:Work>\n",
+       "  </rdf:RDF>\n",
+       " </metadata>\n",
+       " <defs>\n",
+       "  <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
+       " </defs>\n",
+       " <g id=\"figure_1\">\n",
+       "  <g id=\"patch_1\">\n",
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+       "  <g id=\"axes_1\">\n",
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+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "env.vis.figsize = (8, 8)\n",
+    "\n",
+    "# Plot simulator state with the stacked trajectories\n",
+    "_ = env.reset()\n",
+    "img = env.vis.plot_simulator_state(\n",
+    "    env_indices=[1], \n",
+    "    #agent_positions=trajs['agent_positions'],  # Pass stacked trajectories directly\n",
+    "    zoom_radius=70,\n",
+    "    multiple_rollouts=True,\n",
+    "    line_alpha=0.1,          \n",
+    "    line_width=1.0,     \n",
+    "    #weights=trajs['collision_weights'],     \n",
+    "    #colorbar=True, \n",
+    "    plot_guidance_pos_xy=True,\n",
+    ")[0]\n",
+    "\n",
+    "img"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9503a695",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e5af51cf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "gpudrive",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/eval/rollout_test.py b/examples/eval/rollout_test.py
new file mode 100644
index 000000000..39c960423
--- /dev/null
+++ b/examples/eval/rollout_test.py
@@ -0,0 +1,129 @@
+import numpy as np
+import mediapy as media
+import wandb
+
+from gpudrive.env.env_torch import GPUDriveTorchEnv
+from gpudrive.env.config import EnvConfig
+from gpudrive.env.dataset import SceneDataLoader
+from gpudrive.visualize.utils import img_from_fig
+
+
+def test_rollout(focus_agents=[0, 1]):
+    run = wandb.init(project="humanlike_tests", group="rollout_tests")
+
+    env_config = EnvConfig(
+        guidance=True,
+        guidance_mode="log_replay",  # Options: "log_replay", "vbd_amortized"
+        add_reference_pos_xy=True,
+        add_reference_speed=True,
+        add_reference_heading=True,
+        add_previous_action=True,
+        reward_type="guided_autonomy",
+        init_mode="wosac_train",
+        dynamics_model="delta_local",  # "state", #"classic",
+        smoothen_trajectory=True,
+        smoothness_weight=0.001,
+        collision_weight=-0.01,
+        off_road_weight=-0.01,
+        guidance_heading_weight=0.01,
+        guidance_speed_weight=0.01,
+    )
+
+    # Create data loader
+    train_loader = SceneDataLoader(
+        root="data/processed/wosac/selected_json",
+        batch_size=1,
+        dataset_size=100,
+        sample_with_replacement=False,
+        shuffle=False,
+        file_prefix="",
+    )
+
+    # Make env
+    env = GPUDriveTorchEnv(
+        config=env_config,
+        data_loader=train_loader,
+        max_cont_agents=64,  # Number of agents to control
+        device="cuda",
+    )
+
+    control_mask = env.cont_agent_mask
+
+    # Rollout
+    obs = env.reset(mask=control_mask)
+
+    sim_frames = []
+    agent_obs_frames = {i: [] for i in focus_agents}
+    cum_reward = {i: 0 for i in focus_agents}
+
+    expert_actions, _, _, _ = env.get_expert_actions()
+
+    for time_step in range(env.init_steps, env.episode_len):
+        print(f"Step: {env.step_in_world[0, 0, 0].item()}")
+
+        # Step
+        expert_actions, _, _, _ = env.get_expert_actions()
+
+        # Sample random actions
+        # expert_actions = np.random.uniform(
+        #     low=-env_config.max_accel_value,
+
+        env.step_dynamics(expert_actions[:, :, time_step, :])
+
+        obs = env.get_obs(control_mask)
+        reward = env.get_rewards()
+        done = env.get_dones()
+        info = env.get_infos()
+
+        for agent_idx in focus_agents:
+            cum_reward[agent_idx] += reward[0, agent_idx].item()
+
+        # Render
+        if time_step % 5 == 0 or time_step > env.episode_len - 3:
+            sim_states, agent_obs = env.render(focus_agent_idx=focus_agents)
+            sim_frames.append(img_from_fig(sim_states[0]))
+            for i in focus_agents:
+                agent_obs_frames[i].append(img_from_fig(agent_obs[i]))
+
+        # Log reward magnitudes
+        for agent_idx in focus_agents:
+            agent_key = f"agent_{agent_idx}"
+            wandb.log(
+                {
+                    f"{agent_key}/R_combined": reward[0, agent_idx].item(),
+                    f"{agent_key}/R_jerk": env.smoothness_penalty[
+                        0, agent_idx
+                    ].item(),
+                    f"{agent_key}/R_collision": env.base_rewards[
+                        0, agent_idx
+                    ].item(),
+                    f"{agent_key}/R_speed_heading": env.speed_heading_reward[
+                        0, agent_idx
+                    ].item(),
+                    f"{agent_key}/R_route": env.route_reward[
+                        0, agent_idx
+                    ].item(),
+                    f"{agent_key}/R_cumulative": cum_reward[agent_idx],
+                },
+                step=env.step_in_world[0, 0, 0].item(),
+            )
+
+    for agent_idx in focus_agents:
+        agent_key = f"agent_{agent_idx}"
+        agent_obs_arr = np.array(agent_obs_frames[agent_idx])
+        wandb.log(
+            {
+                f"{agent_key}/render": wandb.Video(
+                    np.moveaxis(agent_obs_arr, -1, 1),
+                    fps=5,
+                    format="mp4",
+                )
+            }
+        )
+
+    env.close()
+    run.finish()
+
+
+if __name__ == "__main__":
+    test_rollout(focus_agents=[0, 1, 2, 3, 4])
diff --git a/examples/eval/run_wosac_eval.py b/examples/eval/run_wosac_eval.py
index d243dad66..7657a1941 100644
--- a/examples/eval/run_wosac_eval.py
+++ b/examples/eval/run_wosac_eval.py
@@ -27,6 +27,8 @@
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger("WOSAC evaluation")
+# Suppress excessive logging
+logging.getLogger("tensorflow").setLevel(logging.ERROR)
 
 
 def get_state(env):
@@ -47,7 +49,7 @@ def get_state(env):
     return (
         ego_state.pos_x + mean_x,
         ego_state.pos_y + mean_y,
-        avg_z_pos, #ego_state.pos_z
+        avg_z_pos,  # ego_state.pos_z
         ego_state.rotation_angle,
         ego_state.id,
     )
@@ -62,12 +64,16 @@ def rollout(
     device: str,
     render_simulator_states: bool = False,
     render_agent_pov: bool = False,
-    render_every_n_steps: int = 5,
+    render_every_n_steps: int = 2,
     save_videos: bool = True,
     video_dir: str = "videos",
     video_format: str = "gif",
 ):
     """Rollout agent in the environment and return the scenario rollouts."""
+
+    if save_videos:
+        os.makedirs(video_dir, exist_ok=True)
+
     # Storage
     env_ids = list(range(num_envs))
     simulator_state_frames = {env_id: [] for env_id in range(num_envs)}
@@ -120,7 +126,7 @@ def rollout(
         if render_simulator_states and time_step % render_every_n_steps == 0:
             sim_states = env.vis.plot_simulator_state(
                 env_indices=env_ids,
-                zoom_radius=100,
+                zoom_radius=120,
                 time_steps=[time_step] * len(env_ids),
                 plot_guidance_pos_xy=True,
             )
@@ -184,7 +190,7 @@ def rollout(
     control_mask = control_mask.cpu().numpy()
 
     logging.info(
-        f"Policy rollout took: {perf_counter() - start_env_rollout:.2f} s ({len(env.data_batch)} scenarios)."
+        f"Policy rollout took: {perf_counter() - start_env_rollout:.2f} s (Render = {render_simulator_states}; {len(env.data_batch)} scenarios)."
     )
 
     start_ground_truth_ext = perf_counter()
@@ -234,16 +240,20 @@ def rollout(
 
     # Settings
     MAX_AGENTS = 64
-    NUM_ENVS = 1
-    DEVICE = "cpu"  # where to run the env rollouts
+    NUM_ENVS = 20
+    DEVICE = "cuda"  # where to run the env rollouts
     NUM_ROLLOUTS_PER_BATCH = 1
-    NUM_DATA_BATCHES = 10
+    NUM_DATA_BATCHES = 1
     INIT_STEPS = 10
     DATASET_SIZE = 100
-    RENDER = False
+    RENDER = True
 
     DATA_JSON = "data/processed/wosac/validation_json_100"
     DATA_TFRECORD = "data/processed/wosac/validation_tfrecord_100"
+    # CPT_PATH = "checkpoints/model_guidance_progress__S_1__05_04_17_37_18_741_001677.pt" # .73 meta-score on single_scene (10 rollouts)
+    # https://wandb.ai/emerge_/humanlike/runs/guidance_progress__S_1__05_04_17_37_18_741?nw=nwuserdaphnecor
+
+    CPT_PATH = "checkpoints/model_guidance_progress__S_100__05_06_20_02_22_663_001500.pt"
 
     # Create data loader
     val_loader = SceneDataLoader(
@@ -251,14 +261,12 @@ def rollout(
         batch_size=NUM_ENVS,
         dataset_size=DATASET_SIZE,
         sample_with_replacement=True,
-        shuffle=True,
+        shuffle=False,
         file_prefix="",
     )
 
     # Load agent
-    agent = load_agent(
-        path_to_cpt="checkpoints/model_guidance_log_replay__S_100__04_29_19_49_30_053_015179.pt",
-    ).to(DEVICE)
+    agent = load_agent(path_to_cpt=CPT_PATH).to(DEVICE)
 
     # Override default environment settings to match those the agent was trained with
     default_config = EnvConfig()
@@ -273,7 +281,7 @@ def rollout(
     # Add fixed overrides specific to WOSAC evaluation
     config_dict["init_steps"] = INIT_STEPS
     config_dict["init_mode"] = "wosac_eval"
-   
+
     logging.info(
         f"initializing env with init_mode = {config_dict['init_mode']}"
     )
diff --git a/gpudrive/env/config.py b/gpudrive/env/config.py
index 36d9b7f64..29721cf44 100755
--- a/gpudrive/env/config.py
+++ b/gpudrive/env/config.py
@@ -40,8 +40,8 @@ class EnvConfig:
     add_reference_pos_xy: bool = True  # (x, y) position time series
     add_reference_speed: bool = False  # speed time series
     add_reference_heading: bool = False  # heading time series
-
-    smoothen_trajectory: bool = False  # Filters out the trajectory
+    smoothen_trajectory: bool = True  # Filters out the trajectory
+    guidance_pos_xy_radius: float = 1.0  # Tightness of the positions guidance
 
     # Maximum number of controlled agents in the scene
     max_controlled_agents: int = madrona_gpudrive.kMaxAgentCount
@@ -116,25 +116,16 @@ class EnvConfig:
     # Goal behavior settings
     goal_behavior: str = "ignore"  # Options: "stop", "ignore", "remove"
 
-    prob_reference_dropout: float = (
-        0.0  # Probability of dropping reference points
-    )
-    min_reference_points: int = 1  # Minimum number of reference points
-
     # Reward settings
     reward_type: str = "sparse_on_goal_achieved"
     # Alternatively, "weighted_combination", "guided_autonomy", "reward_conditioned"
 
     # If reward_type is "guided_autonomy", the following parameters are used
-    guidance_sample_interval: int = 1
-    guidance_pos_xy_weight: float = (
-        0.01  # Importance of matching suggested positions
-    )
     guidance_speed_weight: float = (
         0.01  # Importance of matching suggested speeds
     )
     guidance_heading_weight: float = (
-        0.0  # Importance of matching suggested headings
+        0.01  # Importance of matching suggested headings
     )
     smoothness_weight: float = 0.0
 
diff --git a/gpudrive/env/constants.py b/gpudrive/env/constants.py
index a48f5291e..bf5937da4 100644
--- a/gpudrive/env/constants.py
+++ b/gpudrive/env/constants.py
@@ -23,7 +23,7 @@
 MAX_ROAD_SCALE = 100
 
 # Feature shape constants
-EGO_FEAT_DIM = 4  # Ego state base fields
+EGO_FEAT_DIM = 6  # Ego state base fields
 PARTNER_FEAT_DIM = 6
 ROAD_GRAPH_FEAT_DIM = 13
 
diff --git a/gpudrive/env/env_puffer.py b/gpudrive/env/env_puffer.py
index 0d2d740d8..8eac76e00 100644
--- a/gpudrive/env/env_puffer.py
+++ b/gpudrive/env/env_puffer.py
@@ -181,9 +181,8 @@ def __init__(
         add_reference_pos_xy=True,
         add_reference_speed=True,
         add_reference_heading=False,
-        prob_reference_dropout=0.0,
+        smoothen_trajectory=False,
         reward_type="weighted_combination",
-        guidance_pos_xy_weight=0.01,
         guidance_speed_weight=0.0,
         guidance_heading_weight=0.0,
         smoothness_weight=0.0,
@@ -203,7 +202,9 @@ def __init__(
         render=False,
         render_3d=True,
         render_interval=50,
+        render_every_t=5,
         render_k_scenarios=3,
+        render_agent_idx=[0, 6],
         render_agent_obs=False,
         render_format="mp4",
         render_fps=15,
@@ -243,7 +244,9 @@ def __init__(
 
         self.render = render
         self.render_interval = render_interval
+        self.render_every_t = render_every_t
         self.render_k_scenarios = render_k_scenarios
+        self.render_agent_idx = render_agent_idx
         self.render_agent_obs = render_agent_obs
         self.render_format = render_format
         self.render_fps = render_fps
@@ -265,7 +268,6 @@ def __init__(
             road_map_obs=road_map_obs,
             partner_obs=partner_obs,
             reward_type=reward_type,
-            guidance_pos_xy_weight=guidance_pos_xy_weight,
             guidance_speed_weight=guidance_speed_weight,
             guidance_heading_weight=guidance_heading_weight,
             smoothness_weight=smoothness_weight,
@@ -277,7 +279,7 @@ def __init__(
             add_reference_pos_xy=add_reference_pos_xy,
             add_reference_speed=add_reference_speed,
             add_reference_heading=add_reference_heading,
-            prob_reference_dropout=prob_reference_dropout,
+            smoothen_trajectory=smoothen_trajectory,
             dynamics_model=dynamics_model,
             collision_behavior=collision_behavior,
             goal_behavior=goal_behavior,
@@ -321,7 +323,7 @@ def __init__(
         super().__init__()
 
         # Reset the environment and get the initial observations
-        self.observations = self.env.reset(self.controlled_agent_mask)
+        self.observations = self.env.reset(mask=self.controlled_agent_mask)
 
         self.masks = torch.ones(self.num_agents, dtype=bool)
         self.actions = torch.zeros(
@@ -335,9 +337,8 @@ def __init__(
         self.was_rendered_in_rollout = {
             env_idx: True for env_idx in range(render_k_scenarios)
         }
-        self.agent_was_rendered_in_rollout = False
         self.frames = {env_idx: [] for env_idx in range(render_k_scenarios)}
-        self.agent_frames = []
+        self.agent_frames = {agent_idx: [] for agent_idx in render_agent_idx}
 
         self.global_step = 0
         self.iters = 0
@@ -439,7 +440,7 @@ def step(self, action):
         if self.reward_type == "guided_autonomy":
             self.human_like_rewards[
                 self.live_agent_mask
-            ] += self.env.guidance_error[self.live_agent_mask]
+            ] += self.env.guidance_reward[self.live_agent_mask]
             self.internal_rewards[
                 self.live_agent_mask
             ] += self.env.base_rewards[self.live_agent_mask]
@@ -509,8 +510,6 @@ def step(self, action):
                 for render_env_idx in range(self.render_k_scenarios):
                     self.log_video_to_wandb(render_env_idx, done_worlds)
 
-                self.log_agent_obs_to_wandb()
-
             # Log episode statistics
             controlled_mask = self.controlled_agent_mask[
                 done_worlds, :
@@ -565,7 +564,7 @@ def step(self, action):
                 self.info_lst.append(
                     {
                         "metrics/mean_episode_reward_per_agent": agent_episode_returns.mean().item(),
-                        "metrics/mean_imitation_distance": human_like_values.mean().item(),
+                        "metrics/mean_guidance_reward": human_like_values.mean().item(),
                         "metrics/mean_internal_reward": internal_reward_values.mean().item(),
                         "metrics/perc_goal_achieved": goal_achieved_rate.item(),
                         "metrics/perc_off_road": off_road_rate.item(),
@@ -646,7 +645,11 @@ def render_env(self):
         )
         time_steps = list(self.episode_lengths[envs_to_render, 0])
 
-        if len(envs_to_render) > 0:
+        render_at_time = bool(
+            self.env.step_in_world[0, 0] % self.render_every_t == 0
+        )
+
+        if len(envs_to_render) > 0 and render_at_time:
             sim_state_figures = self.env.vis.plot_simulator_state(
                 env_indices=envs_to_render,
                 time_steps=time_steps,
@@ -654,13 +657,18 @@ def render_env(self):
                 plot_guidance_pos_xy=self.plot_guidance_pos_xy,
             )
 
-            agent_obs = self.env.vis.plot_agent_observation(
-                env_idx=0,
-                agent_idx=0,
-                figsize=(10, 10),
-                trajectory=self.env.reference_path[0, :, :].to("cpu"),
-            )
-            self.agent_frames.append(img_from_fig(agent_obs))
+            for agent_idx in self.render_agent_idx:
+                agent_obs = self.env.vis.plot_agent_observation(
+                    env_idx=0,
+                    agent_idx=0,
+                    figsize=(10, 10),
+                    trajectory=self.env.reference_path[agent_idx, :, :].to(
+                        "cpu"
+                    ),
+                    step_reward=self.env.guidance_reward[0, agent_idx].item(),
+                    route_progress=self.env.route_progress[agent_idx].item(),
+                )
+                self.agent_frames[agent_idx].append(img_from_fig(agent_obs))
 
             for idx, render_env_idx in enumerate(envs_to_render):
                 self.frames[render_env_idx].append(
@@ -681,36 +689,20 @@ def resample_scenario_batch(self):
 
         self.reset()  # Reset storage
         # Get info from new worlds
-        self.observations = self.env.reset(self.controlled_agent_mask)
+        self.observations = self.env.reset(mask=self.controlled_agent_mask)
 
         self.log_data_coverage()
 
-    def clear_render_storage(self):
+    def clear_render_storage(self, env_list_to_clear=None):
         """Clear rendering storage."""
-        for env_idx in range(self.render_k_scenarios):
+        for env_idx in env_list_to_clear:
             self.frames[env_idx] = []
-            self.agent_frames = []
             self.rendering_in_progress[env_idx] = False
             self.was_rendered_in_rollout[env_idx] = False
-            self.agent_was_rendered_in_rollout = False
 
-    def log_agent_obs_to_wandb(self):
-        """Log agent observation to wandb."""
-
-        frames_array = np.array(self.agent_frames)
-        if frames_array.shape[0] > 10:
-            self.wandb_obj.log(
-                {
-                    f"vis/agent_obs/env_0": wandb.Video(
-                        np.moveaxis(frames_array, -1, 1),
-                        fps=self.render_fps,
-                        format=self.render_format,
-                        caption=f"global step: {self.global_step:,}",
-                    )
-                }
-            )
-            self.agent_frames = []
-            self.agent_was_rendered_in_rollout = True
+        if env_list_to_clear is not None:
+            for agent_idx in self.render_agent_idx:
+                self.agent_frames[agent_idx] = []
 
     def log_video_to_wandb(self, render_env_idx, done_worlds):
         """Log arrays as videos to wandb."""
@@ -729,8 +721,23 @@ def log_video_to_wandb(self, render_env_idx, done_worlds):
                     )
                 }
             )
+            for agent_idx in self.render_agent_idx:
+                agent_frames_array = np.array(self.agent_frames[agent_idx])
+                self.wandb_obj.log(
+                    {
+                        f"vis/agent_obs/env_0_agent_{agent_idx}": wandb.Video(
+                            np.moveaxis(agent_frames_array, -1, 1),
+                            fps=self.render_fps,
+                            format=self.render_format,
+                            caption=f"global step: {self.global_step:,}",
+                        )
+                    }
+                )
+
             # Reset rendering storage
             self.frames[render_env_idx] = []
+            for agent_idx in self.render_agent_idx:
+                self.agent_frames[agent_idx] = []
             self.rendering_in_progress[render_env_idx] = False
             self.was_rendered_in_rollout[render_env_idx] = True
 
@@ -776,119 +783,131 @@ def compute_realism_metrics(self, done_worlds):
         control_mask = (
             self.controlled_agent_mask[done_worlds].detach().cpu().numpy()
         )
-        # [batch, time, 1]
-        valid_mask = (
-            self.env.reference_trajectory.valids[done_worlds]
-            .detach()
-            .cpu()
-            .numpy()[control_mask]
-            .squeeze(-1)
-        ).astype(bool)
-
-        # Take human logs (ground-truth)
-        # Shape: [worlds, max_cont_agents, time, 2] -> [batch, time, 2]
-        ref_pos_xy_np = (
-            self.env.reference_trajectory.pos_xy[done_worlds]
-            .detach()
-            .cpu()
-            .numpy()[control_mask]
-        )
-        # Shape: [worlds, max_cont_agents, time, 1] -> [batch, time, 1]
-        ref_headings_np = (
-            self.env.reference_trajectory.yaw[done_worlds]
-            .detach()
-            .cpu()
-            .numpy()[control_mask]
-            .squeeze(-1)
-        )
 
-        agent_headings_np = (
-            self.headings[done_worlds].detach().cpu().numpy()[control_mask]
-        )
-        agent_pos_xyz_np = (
-            self.pos_xyz[done_worlds].detach().cpu().numpy()[control_mask]
-        )
+        if control_mask.sum() > 0:
+            # [batch, time, 1]
+            valid_mask = (
+                self.env.reference_trajectory.valids[done_worlds]
+                .detach()
+                .cpu()
+                .numpy()[control_mask]
+                .squeeze(-1)
+            ).astype(bool)
+
+            # Take human logs (ground-truth)
+            # Shape: [worlds, max_cont_agents, time, 2] -> [batch, time, 2]
+            ref_pos_xy_np = (
+                self.env.reference_trajectory.pos_xy[done_worlds]
+                .detach()
+                .cpu()
+                .numpy()[control_mask]
+            )
+            # Shape: [worlds, max_cont_agents, time, 1] -> [batch, time, 1]
+            ref_headings_np = (
+                self.env.reference_trajectory.yaw[done_worlds]
+                .detach()
+                .cpu()
+                .numpy()[control_mask]
+                .squeeze(-1)
+            )
 
-        # Extract x, y components (z is not informative)
-        agent_x_np = agent_pos_xyz_np[..., 0]
-        agent_y_np = agent_pos_xyz_np[..., 1]
+            agent_headings_np = (
+                self.headings[done_worlds].detach().cpu().numpy()[control_mask]
+            )
+            agent_pos_xyz_np = (
+                self.pos_xyz[done_worlds].detach().cpu().numpy()[control_mask]
+            )
 
-        ref_x_np = ref_pos_xy_np[..., 0]
-        ref_y_np = ref_pos_xy_np[..., 1]
+            # Extract x, y components (z is not informative)
+            agent_x_np = agent_pos_xyz_np[..., 0]
+            agent_y_np = agent_pos_xyz_np[..., 1]
 
-        # Step duration in seconds
-        seconds_per_step = 0.1  # Assuming 10Hz sampling rate
+            ref_x_np = ref_pos_xy_np[..., 0]
+            ref_y_np = ref_pos_xy_np[..., 1]
 
-        # Compute the metrics for agent trajectories
-        (
-            agent_linear_speed,
-            agent_linear_accel,
-            agent_angular_speed,
-            agent_angular_accel,
-        ) = compute_kinematic_features_np(
-            agent_x_np, agent_y_np, agent_headings_np, seconds_per_step
-        )
+            # Step duration in seconds
+            seconds_per_step = 0.1  # Assuming 10Hz sampling rate
 
-        # Compute the metrics for reference trajectories
-        (
-            ref_linear_speed,
-            ref_linear_accel,
-            ref_angular_speed,
-            ref_angular_accel,
-        ) = compute_kinematic_features_np(
-            ref_x_np, ref_y_np, ref_headings_np, seconds_per_step
-        )
+            # Compute the metrics for agent trajectories
+            (
+                agent_linear_speed,
+                agent_linear_accel,
+                agent_angular_speed,
+                agent_angular_accel,
+            ) = compute_kinematic_features_np(
+                agent_x_np, agent_y_np, agent_headings_np, seconds_per_step
+            )
 
-        # Check which time steps are valid
-        speed_valid, accel_valid = compute_kinematic_validity_np(valid_mask)
+            # Compute the metrics for reference trajectories
+            (
+                ref_linear_speed,
+                ref_linear_accel,
+                ref_angular_speed,
+                ref_angular_accel,
+            ) = compute_kinematic_features_np(
+                ref_x_np, ref_y_np, ref_headings_np, seconds_per_step
+            )
 
-        # Calculate absolute diffs between agent and reference trajectories
-        linear_speed_error = np.abs(agent_linear_speed - ref_linear_speed)
-        linear_accel_error = np.abs(agent_linear_accel - ref_linear_accel)
-        angular_speed_error = np.abs(agent_angular_speed - ref_angular_speed)
-        angular_accel_error = np.abs(agent_angular_accel - ref_angular_accel)
+            # Check which time steps are valid
+            speed_valid, accel_valid = compute_kinematic_validity_np(
+                valid_mask
+            )
 
-        # Calculate displacement error
-        displacement_error = compute_displacement_error_np(
-            agent_x_np, agent_y_np, ref_x_np, ref_y_np
-        )
+            # Calculate absolute diffs between agent and reference trajectories
+            linear_speed_error = np.abs(agent_linear_speed - ref_linear_speed)
+            linear_accel_error = np.abs(agent_linear_accel - ref_linear_accel)
+            angular_speed_error = np.abs(
+                agent_angular_speed - ref_angular_speed
+            )
+            angular_accel_error = np.abs(
+                agent_angular_accel - ref_angular_accel
+            )
 
-        # Apply validity masks
-        masked_speed_error = np.ma.array(linear_speed_error, mask=~speed_valid)
-        masked_accel_error = np.ma.array(linear_accel_error, mask=~accel_valid)
-        masked_angular_speed_error = np.ma.array(
-            angular_speed_error, mask=~speed_valid
-        )
-        masked_angular_accel_error = np.ma.array(
-            angular_accel_error, mask=~accel_valid
-        )
+            # Calculate displacement error
+            displacement_error = compute_displacement_error_np(
+                agent_x_np, agent_y_np, ref_x_np, ref_y_np
+            )
 
-        # Compute MAEs
-        mean_linear_speed_error = masked_speed_error.mean()
-        mean_linear_accel_error = masked_accel_error.mean()
-        mean_angular_speed_error = masked_angular_speed_error.mean()
-        mean_angular_accel_error = masked_angular_accel_error.mean()
-
-        self.info_lst.append(
-            {
-                "realism/kinematic_linear_speed_mae": mean_linear_speed_error,
-                "realism/kinematic_linear_accel_mae": mean_linear_accel_error,
-                "realism/kinematic_angular_speed_mae": mean_angular_speed_error,
-                "realism/kinematic_angular_accel_mae": mean_angular_accel_error,
-                "realism/mean_displacement_error": displacement_error[
-                    valid_mask
-                ].mean(),
-                # "realism/kinematic_ref_linear_speed_dist": wandb.Histogram(
-                #     ref_linear_speed[speed_valid]
-                # ),
-                # "realism/kinematic_ref_linear_accel_dist": wandb.Histogram(
-                #     ref_linear_accel[accel_valid]
-                # ),
-                # "realism/kinematic_agent_linear_speed_dist": wandb.Histogram(
-                #     agent_linear_speed[speed_valid]
-                # ),
-                # "realism/kinematic_agent_linear_accel_dist": wandb.Histogram(
-                #     agent_linear_speed[accel_valid]
-                # ),
-            },
-        )
+            # Apply validity masks
+            masked_speed_error = np.ma.array(
+                linear_speed_error, mask=~speed_valid
+            )
+            masked_accel_error = np.ma.array(
+                linear_accel_error, mask=~accel_valid
+            )
+            masked_angular_speed_error = np.ma.array(
+                angular_speed_error, mask=~speed_valid
+            )
+            masked_angular_accel_error = np.ma.array(
+                angular_accel_error, mask=~accel_valid
+            )
+
+            # Compute MAEs
+            mean_linear_speed_error = masked_speed_error.mean()
+            mean_linear_accel_error = masked_accel_error.mean()
+            mean_angular_speed_error = masked_angular_speed_error.mean()
+            mean_angular_accel_error = masked_angular_accel_error.mean()
+
+            self.info_lst.append(
+                {
+                    "realism/kinematic_linear_speed_mae": mean_linear_speed_error,
+                    "realism/kinematic_linear_accel_mae": mean_linear_accel_error,
+                    "realism/kinematic_angular_speed_mae": mean_angular_speed_error,
+                    "realism/kinematic_angular_accel_mae": mean_angular_accel_error,
+                    "realism/mean_displacement_error": displacement_error[
+                        valid_mask
+                    ].mean(),
+                    "realism/kinematic_ref_linear_speed_dist": wandb.Histogram(
+                        ref_linear_speed[speed_valid]
+                    ),
+                    "realism/kinematic_ref_linear_accel_dist": wandb.Histogram(
+                        ref_linear_accel[accel_valid]
+                    ),
+                    "realism/kinematic_agent_linear_speed_dist": wandb.Histogram(
+                        agent_linear_speed[speed_valid]
+                    ),
+                    "realism/kinematic_agent_linear_accel_dist": wandb.Histogram(
+                        agent_linear_speed[accel_valid]
+                    ),
+                },
+            )
diff --git a/gpudrive/env/env_torch.py b/gpudrive/env/env_torch.py
index 89ed38802..5c723c2e5 100755
--- a/gpudrive/env/env_torch.py
+++ b/gpudrive/env/env_torch.py
@@ -73,6 +73,12 @@ def __init__(
         # Get the initial data batch (set of traffic scenarios)
         self.data_batch = next(self.data_iterator)
 
+        assert self.num_worlds == len(
+            self.data_batch
+        ), f"Number of scenarios in data_batch ({len(self.data_batch)}) \
+        should equal number of worlds ({self.num_worlds}). \
+        \n Please check your data loader configuration."
+
         # Initialize simulator
         self.sim = self._initialize_simulator(params, self.data_batch)
 
@@ -118,6 +124,7 @@ def __init__(
             dtype=np.float32,
         )
 
+        # Action space setup
         self._setup_action_space(action_type)
         self.single_action_space = self.action_space
         self.num_agents = self.cont_agent_mask.sum().item()
@@ -195,6 +202,9 @@ def setup_guidance(self):
                 self.reference_trajectory
             )
 
+        # Initialize the reference trajectory positions that are already in reach
+        self.guidance_points_hit, _ = self.guidance_points_within_reach()
+
     def _load_vbd_model(self, model_path):
         """
         Load the Versatile Behavior Diffusion (VBD) weights from checkpoint.
@@ -448,11 +458,15 @@ def reset(
             mask: Optional mask indicating which agents to return observations for
             env_idx_list: Optional list of environment indices to reset
             condition_mode: Determines how reward weights are sampled:
-                        - "random": Random sampling within bounds (default for training)
-                        - "fixed": Use predefined agent_type weights (for testing)
-                        - "preset": Use a specific preset from agent_type parameter
+                - "random": Random sampling within bounds (default for training)
+                - "fixed": Use predefined agent_type weights (for testing)
+                - "preset": Use a specific preset from agent_type parameter
             agent_type: Specifies which preset weights to use or custom weights
+
+        Returns:
+            obs: The initial observations.
         """
+        # Reset the simulator state
         if env_idx_list is not None:
             self.sim.reset(env_idx_list)
         else:
@@ -486,8 +500,13 @@ def reset(
 
             # Zero out only the reset environments
             self.previous_action_value_tensor[reset_mask] = 0.0
+            # Reset the guidance points hit tensor for the reset environments
+            # TODO: Fix for asynchronous resets
+            self.guidance_points_hit, _ = self.guidance_points_within_reach()
+
         else:
             self.previous_action_value_tensor.zero_()
+            self.guidance_points_hit, _ = self.guidance_points_within_reach()
 
         return self.get_obs(mask)
 
@@ -516,9 +535,9 @@ def get_infos(self):
 
     def get_rewards(
         self,
-        collision_weight=-0.5,
+        collision_weight=-0.01,
         goal_achieved_weight=1.0,
-        off_road_weight=-0.5,
+        off_road_weight=-0.01,
     ):
         """Obtain the rewards for the current step."""
 
@@ -526,8 +545,8 @@ def get_rewards(
         info_tensor = self.sim.info_tensor().to_torch().clone()
         off_road = info_tensor[:, :, 0].to(torch.float)
 
-        # True if the vehicle is in collision with another road object
-        # (i.e. a cyclist or pedestrian)
+        # True if the agent is in collision with another road object
+        # (i.e. a cyclist, pedestrian or vehicle)
         collided = info_tensor[:, :, 1:3].to(torch.float).sum(axis=2)
         goal_achieved = info_tensor[:, :, 3].to(torch.float)
 
@@ -573,58 +592,202 @@ def get_rewards(
 
         elif self.config.reward_type == "guided_autonomy":
 
+            step_in_world = self.step_in_world[:, 0, :].squeeze(-1)
+
+            # 1. Get base rewards defined by user
             self.base_rewards = (
                 collision_weight * collided + off_road_weight * off_road
             )
 
-            step_in_world = self.step_in_world[:, 0, :].squeeze(-1)
+            is_valid = self.reference_trajectory.valids.squeeze(-1)
 
-            # Assumption: All worlds are at the same time step
-            # Check if we still have referene trajectory points, if not
-            # we set the guidance errors to zero.
-            if step_in_world[0] < self.reference_traj_len:
-                batch_indices = torch.arange(step_in_world.shape[0])
+            # 2. Route guidance (where to go)
+            # a) Get the reference trajectory positions
+            (
+                points_within_reach,
+                distance_to_points,
+            ) = self.guidance_points_within_reach()
+
+            if step_in_world[0].item() == 1:
+                # This is the first step, and we reward the agent for
+                # reaching the initial guidance points
+                new_hits = self.guidance_points_hit & is_valid
+            else:
+                # Find waypoints within reach that haven't been hit yet and are valid
+                potential_new_hits = (
+                    points_within_reach
+                    & (~self.guidance_points_hit)
+                    & is_valid
+                ).to(self.device)
+
+                # Create a mask to handle cases where there are no potential hits
+                has_potential_hits = torch.any(
+                    potential_new_hits, dim=2, keepdim=True
+                )
+
+                # Replace distances for non-potential hits with a large value
+                masked_distances = torch.where(
+                    potential_new_hits.bool(),
+                    distance_to_points,
+                    torch.ones_like(distance_to_points) * 1e10,
+                ).to(self.device)
+
+                # Find indices of closest waypoints for each agent
+                closest_indices = torch.argmin(masked_distances, dim=2)
+
+                # Create a one-hot tensor marking only the closest waypoint for each agent
+                (
+                    batch_size,
+                    num_agents,
+                    num_waypoints,
+                ) = potential_new_hits.shape
+                batch_indices = (
+                    torch.arange(batch_size)
+                    .view(-1, 1)
+                    .repeat(1, num_agents)
+                    .flatten()
+                    .to(self.device)
+                )
+                agent_indices = (
+                    torch.arange(num_agents).repeat(batch_size).to(self.device)
+                )
+
+                # Initialize new_hits tensor with zeros
+                new_hits = torch.zeros_like(potential_new_hits)
+
+                # Only set closest hits where there are potential hits
+                valid_agents = has_potential_hits.view(-1)
+
+                # Efficiently set the closest point for each valid agent
+                if torch.any(valid_agents):
+                    valid_batch_indices = batch_indices[valid_agents]
+                    valid_agent_indices = agent_indices[valid_agents]
+                    valid_closest_indices = closest_indices.view(-1)[
+                        valid_agents
+                    ]
+
+                    new_hits[
+                        valid_batch_indices,
+                        valid_agent_indices,
+                        valid_closest_indices,
+                    ] = True
+
+            # Update the guidance points hit tensor
+            self.guidance_points_hit = self.guidance_points_hit | new_hits
+
+            # Count the number of new waypoints hit in this step
+            guidance_points_hit_count = new_hits.sum(dim=-1).float()
+
+            # Reward agent for successfully reaching guidance points along the route
+            # Scale reward by 1/num_valid_points to ensure maximum attainable reward is 1.0
+            # We do this for a consistent reward magnitude regardless of route length or density
+            self.route_reward = torch.clamp(
+                guidance_points_hit_count
+                / (self.reference_trajectory.valids.sum(axis=[2, 3]) + 1e-5),
+                min=0,
+                max=1,
+            )
+
+            # We want agents that are parked to stay parked, and generally
+            # we want agents to don't go beyond their reference trajectory.
+            # To do this, add a penalty proportional to the agent position to
+            # the end position of the trajectory at the end of the episode
+            total_valid = self.reference_trajectory.valids.clone().sum(
+                axis=[2, 3]
+            )
+            valid_hits_so_far = self.guidance_points_hit.clone().sum(dim=[-1])
+
+            # Handle the case where there are zero valid points
+            # If total_valid is 0, we consider the route 100% complete (progress = 1.0)
+            route_progress = torch.where(
+                total_valid > 0,
+                valid_hits_so_far / (total_valid + 1e-5),  # Normal case
+                torch.ones_like(
+                    valid_hits_so_far
+                ),  # When agent has no valid points
+            )
+
+            completed_route_mask = (route_progress >= 0.99).float()
 
-                # Guidance
-                suggested_pos_xy = self.reference_trajectory.pos_xy[
-                    batch_indices, :, step_in_world, :
+            # b). Add a bonus for being close to the reference end position
+            if torch.any(completed_route_mask > 0):
+                # Extract the last valid reference position for each world, agent
+                # valid_mask is [1, 64, 91] and reference_trajectory.pos_xy is [1, 64, 91, 2]
+                # We need to find the last valid position for each world-agent pair
+                agent_states = GlobalEgoState.from_tensor(
+                    self.sim.absolute_self_observation_tensor(),
+                    self.backend,
+                    self.device,
+                )
+
+                # Get indices of last valid positions for each reference trajectory
+                last_valid_indices = torch.argmax(
+                    is_valid
+                    * torch.arange(is_valid.shape[2], device=is_valid.device),
+                    dim=2,
+                )
+
+                # Create indices for gathering
+                batch_indices = torch.zeros_like(
+                    last_valid_indices
+                )  # Shape: [1, 64]
+                agent_indices = torch.arange(
+                    is_valid.shape[1], device=is_valid.device
+                ).expand_as(
+                    last_valid_indices
+                )  # Shape: [1, 64]
+
+                # Gather the last valid reference positions
+                last_valid_positions = self.reference_trajectory.pos_xy[
+                    batch_indices, agent_indices, last_valid_indices
                 ]
 
+                distance = torch.norm(
+                    agent_states.pos_xy - last_valid_positions, dim=2
+                )
+
+                # Add bonus for being close to the reference end position
+                # Using exponential decay: bonus = scale * exp(-distance/th)
+                distance_threshold = 0.5
+                max_position_bonus = 0.01
+
+                position_bonus = max_position_bonus * torch.exp(
+                    -distance / distance_threshold
+                )
+
+                position_bonus = position_bonus * completed_route_mask
+
+                self.route_reward += position_bonus
+
+            self.guidance_reward = self.route_reward.clone()
+
+            # 3. Speed and heading targets
+            if step_in_world[0] < self.reference_traj_len:
+                batch_idx = torch.arange(step_in_world.shape[0])
+
                 suggested_speed = self.reference_trajectory.ref_speed[
-                    batch_indices, :, step_in_world
+                    batch_idx, :, step_in_world
                 ].squeeze(-1)
 
                 suggested_heading = self.reference_trajectory.yaw[
-                    batch_indices, :, step_in_world
+                    batch_idx, :, step_in_world
                 ].squeeze(-1)
 
-                is_valid = (
+                valid_points = (
                     self.reference_trajectory.valids[
-                        batch_indices, :, step_in_world
+                        batch_idx, :, step_in_world
                     ]
                     .squeeze(-1)
                     .bool()
                 )
 
-                # Get actual agent positions
-                agent_states = GlobalEgoState.from_tensor(
-                    self.sim.absolute_self_observation_tensor(),
-                    self.backend,
-                    self.device,
-                )
-
-                actual_agent_pos_xy = agent_states.pos_xy
-
                 actual_agent_speed = (
                     self.sim.self_observation_tensor().to_torch()[:, :, 0]
                 )
 
-                actual_agent_heading = agent_states.rotation_angle
+                actual_agent_heading = self.agent_states.rotation_angle
 
                 # Compute distances
-                guidance_pos_error = torch.norm(
-                    suggested_pos_xy - actual_agent_pos_xy, dim=-1
-                )
                 guidance_speed_error = (
                     suggested_speed - actual_agent_speed
                 ) ** 2
@@ -632,58 +795,72 @@ def get_rewards(
                     suggested_heading - actual_agent_heading
                 ) ** 2
 
-                self.guidance_error = (
-                    -self.config.guidance_pos_xy_weight
-                    * torch.log(guidance_pos_error + 1.0)
-                    - self.config.guidance_speed_weight
-                    * torch.log(guidance_speed_error + 1.0)
+                guidance_speed_penalty = 1.0 - torch.exp(
+                    -guidance_speed_error + 1e-8
+                )
+                guidance_heading_penalty = 1.0 - torch.exp(
+                    -guidance_heading_error + 1e-8
+                )
+
+                speed_heading_reward = (
+                    -self.config.guidance_speed_weight * guidance_speed_penalty
                     - self.config.guidance_heading_weight
-                    * torch.log(guidance_heading_error + 1.0)
+                    * guidance_heading_penalty
                 )
 
-                # Zero-out guidance errors for invalid time steps, that is,
-                # those that were not observed at the current time step
-                self.guidance_error[~is_valid] = 0.0
-
-                # Reduce guidance density
-                if self.config.guidance_sample_interval > 1:
-                    waypoint_mask = (
-                        (
-                            step_in_world
-                            % self.config.guidance_sample_interval
-                            == 0
-                        )
-                        .float()
-                        .unsqueeze(1)
-                    )
-                    self.guidance_error = self.guidance_error * waypoint_mask
+                speed_heading_reward[~valid_points] = 0.0
 
-            else:
-                self.guidance_error = torch.zeros_like(self.base_rewards)
+                self.speed_heading_reward = speed_heading_reward.clone()
+
+                self.guidance_reward += self.speed_heading_reward
 
-            # Encourage smooth driving
+            # 4. Penalty for action jerk
             if hasattr(self, "action_diff"):
                 acceleration_jerk = (
                     self.action_diff[:, :, 0] ** 2
                 )  # First action component is acceleration
                 steering_jerk = (
                     self.action_diff[:, :, 1] ** 2
-                )  # Second action component is steering
+                )  # Second action component is steering angle
 
-                self.smoothness_penalty = -(
-                    self.config.smoothness_weight * acceleration_jerk
-                    + self.config.smoothness_weight * steering_jerk
+                # Small jerks: penalty is close to x (approximately linear)
+                # Large jerks: penalty approaches 1.0 (saturates)
+                acceleration_penalty = 1.0 - torch.exp(-acceleration_jerk)
+                steering_penalty = 1.0 - torch.exp(-steering_jerk)
+
+                self.smoothness_penalty = -self.config.smoothness_weight * (
+                    acceleration_penalty + steering_penalty
                 )
             else:
                 self.smoothness_penalty = torch.zeros_like(self.base_rewards)
 
-            self.guidance_error += self.smoothness_penalty
+            self.guidance_reward += self.smoothness_penalty
 
-            # Combine base rewards with guidance error
-            rewards = self.base_rewards + self.guidance_error
+            # Combine
+            rewards = self.base_rewards + self.guidance_reward
 
             return rewards
 
+    def guidance_points_within_reach(self):
+        # Get actual agent positions
+        self.agent_states = GlobalEgoState.from_tensor(
+            self.sim.absolute_self_observation_tensor(),
+            self.backend,
+            self.device,
+        )
+
+        # Calculate Euclidean distance to all reference positions
+        # Output shape: [worlds, agents, reference_traj_len]
+        distances = torch.norm(
+            self.agent_states.pos_xy.unsqueeze(2)
+            - self.reference_trajectory.pos_xy,
+            dim=-1,
+        )
+
+        points_within_reach = distances < self.config.dist_to_goal_threshold
+
+        return points_within_reach, distances
+
     def step_dynamics(self, actions):
         if actions is not None:
             self._apply_actions(actions)
@@ -867,23 +1044,6 @@ def _get_guidance(self, mask=None) -> torch.Tensor:
 
             valid_timesteps_mask = self.reference_trajectory.valids.bool()
 
-            # Provide agent with index to pay attention to through one-hot encoding
-            next_step_in_world = torch.clamp(
-                self.step_in_world[:, 0, :].squeeze(-1) + 1,
-                min=0,
-                max=self.reference_traj_len - 1,
-            )
-            time_one_hot = torch.zeros(
-                (
-                    self.num_worlds,
-                    self.max_agent_count,
-                    self.reference_traj_len,
-                    1,
-                ),
-                device=self.device,
-            )
-            time_one_hot[:, :, next_step_in_world, :] = 1.0
-
             if self.config.add_reference_speed:
                 reference_speed = (
                     self.reference_trajectory.ref_speed.clone()
@@ -935,14 +1095,9 @@ def _get_guidance(self, mask=None) -> torch.Tensor:
                 ] = constants.INVALID_ID
 
                 # Make unnormalized reference path available for plotting
-                self.reference_path = torch.cat(
-                    (local_ref_xy_orig, time_one_hot), dim=-1
-                )
+                self.reference_path = local_ref_xy_orig
 
-                reference_path = torch.cat(
-                    (local_reference_xy, time_one_hot), dim=-1
-                )
-                guidance.append(reference_path)
+                guidance.append(local_reference_xy)
 
             if self.config.add_reference_heading:
                 reference_headings = self.reference_trajectory.yaw.clone()
@@ -970,19 +1125,6 @@ def _get_guidance(self, mask=None) -> torch.Tensor:
 
         else:
             batch_size = mask.sum()
-            batch_indices = torch.arange(batch_size)
-
-            # Provide agent with index to pay attention to through one-hot encoding
-            next_step_in_world = torch.clamp(
-                self.step_in_world[mask] + 1,
-                min=0,
-                max=self.reference_traj_len - 1,
-            )
-            time_one_hot = torch.zeros(
-                (batch_size, self.reference_traj_len, 1),
-                device=self.device,
-            )
-            time_one_hot[batch_indices, next_step_in_world] = 1.0
 
             valid_timesteps_mask = self.reference_trajectory.valids.bool()[
                 mask
@@ -1043,14 +1185,10 @@ def _get_guidance(self, mask=None) -> torch.Tensor:
                     ~valid_timesteps_mask.expand_as(local_reference_xy)
                 ] = constants.INVALID_ID
 
-                # Stack
-                reference_path = torch.cat(
-                    (local_reference_xy, time_one_hot), dim=2
-                )
+                reference_path = local_reference_xy
+
+                self.reference_path = local_reference_xy_orig
 
-                self.reference_path = torch.cat(
-                    (local_reference_xy_orig, time_one_hot), dim=2
-                )
                 guidance.append(reference_path)
 
             if self.config.add_reference_heading:
@@ -1104,6 +1242,28 @@ def _get_ego_state(self, mask=None) -> torch.Tensor:
 
         if mask is None:
 
+            # New: Give agent sense of progress / history
+            # 1. Overall trajectory progress (percentage of valid points hit)
+            total_valid = self.reference_trajectory.valids.clone().sum(
+                axis=[2, 3]
+            )
+            valid_hits_so_far = self.guidance_points_hit.clone().sum(dim=[-1])
+
+            # If total_valid is 0, we consider the route 100% complete (progress = 1.0)
+            self.route_progress = torch.where(
+                total_valid > 0,
+                valid_hits_so_far / (total_valid + 1e-5),  # Normal case
+                torch.ones_like(
+                    valid_hits_so_far
+                ),  # When agent has no valid points
+            )
+
+            # 2. How much time do I have left to complete the route
+            normalized_time = self.step_in_world.clone() / self.episode_len
+
+            base_fields.append(self.route_progress.unsqueeze(-1))
+            base_fields.append(normalized_time)
+
             if self.config.add_previous_action:
                 normalized_prev_actions = (
                     self.previous_action_value_tensor[:, :, :2]
@@ -1112,7 +1272,6 @@ def _get_ego_state(self, mask=None) -> torch.Tensor:
                 base_fields.append(normalized_prev_actions)
 
             if self.config.reward_type == "reward_conditioned":
-
                 full_fields = base_fields + [
                     self.reward_weights_tensor.expand(self.num_worlds, -1)
                 ]
@@ -1121,10 +1280,41 @@ def _get_ego_state(self, mask=None) -> torch.Tensor:
                 return torch.cat(base_fields, dim=-1)
         else:
 
-            base_fields.append(
-                self.previous_action_value_tensor[mask][:, :2]
-                / constants.MAX_ACTION_VALUE,  # Previous accel, steering
+            # New: Give agent sense of progress / history
+            # 1. Overall trajectory progress (percentage of valid points hit)
+            total_valid = (
+                self.reference_trajectory.valids[mask]
+                .clone()
+                .sum(dim=1)
+                .squeeze(-1)
+            )
+            valid_hits_so_far = self.guidance_points_hit.clone()[mask].sum(
+                dim=-1
+            )
+
+            # Handle the case where there are zero valid points
+            # If total_valid is 0, we consider the route 100% complete (progress = 1.0)
+            self.route_progress = torch.where(
+                total_valid > 0,
+                valid_hits_so_far / (total_valid + 1e-5),  # Normal case
+                torch.ones_like(
+                    valid_hits_so_far
+                ),  # When agent has no valid points
             )
+
+            # 2. How much time do I have left
+            normalized_time = self.step_in_world[mask] / self.episode_len
+
+            base_fields.append(self.route_progress.unsqueeze(-1))
+            base_fields.append(normalized_time)
+
+            if self.config.add_previous_action:
+                normalized_prev_actions = (
+                    self.previous_action_value_tensor[:, :, :2][mask]
+                    / constants.MAX_ACTION_VALUE
+                )
+                base_fields.append(normalized_prev_actions)
+
             if self.config.reward_type == "reward_conditioned":
                 _, agent_indices = torch.where(mask)
                 weights_for_masked_agents = self.reward_weights_tensor.to(
@@ -1481,26 +1671,56 @@ def get_scenario_ids(self):
 
         return scenario_ids
 
+    def render(self, focus_env_idx=0, focus_agent_idx=[0, 1]):
+        """Quick rendering function for debugging."""
+
+        sim_states = self.vis.plot_simulator_state(
+            env_indices=[focus_env_idx],
+            zoom_radius=70,
+            time_steps=[self.step_in_world[0, 0, 0].item()],
+            plot_guidance_pos_xy=True,
+        )
+
+        agent_views = []
+        for agent_idx in focus_agent_idx:
+            agent_obs = self.vis.plot_agent_observation(
+                env_idx=focus_env_idx,
+                agent_idx=agent_idx,
+                figsize=(10, 10),
+                trajectory=self.reference_path[agent_idx, :, :],
+                step_reward=self.guidance_reward[
+                    focus_env_idx, agent_idx
+                ].item(),
+                route_progress=self.route_progress[agent_idx],
+            )
+            agent_views.append(agent_obs)
+
+        return sim_states, agent_views
+
 
 if __name__ == "__main__":
 
+    FOCUS_AGENTS = [0, 1, 2, 3]
+
     env_config = EnvConfig(
         guidance=True,
         guidance_mode="log_replay",  # Options: "log_replay", "vbd_amortized"
         add_reference_pos_xy=True,
-        add_reference_speed=True,
-        add_reference_heading=True,
+        add_reference_speed=False,
+        add_reference_heading=False,
         reward_type="guided_autonomy",
         init_mode="wosac_train",
-        smoothen_trajectory=True,
+        dynamics_model="delta_local",  # "state", #"classic",
+        smoothen_trajectory=False,
+        add_previous_action=True,
     )
     render_config = RenderConfig()
 
     # Create data loader
     train_loader = SceneDataLoader(
-        root="data/processed/wosac/debug",
-        batch_size=5,
-        dataset_size=1,
+        root="data/processed/wosac/validation_json_1",
+        batch_size=1,
+        dataset_size=100,
         sample_with_replacement=False,
         shuffle=False,
         file_prefix="",
@@ -1518,79 +1738,46 @@ def get_scenario_ids(self):
 
     print(f"Number of controlled agents: {control_mask.sum()}")
 
-    metadata = Metadata.from_tensor(
-        env.sim.metadata_tensor(),
-        backend=env.backend,
-        device=env.device,
-    )
-    print(f"avg Z: {metadata.avg_z}")
-
     # Rollout
     obs = env.reset(mask=control_mask)
 
     sim_frames = []
-    agent_obs_frames = []
+    agent_obs_frames = {i: [] for i in FOCUS_AGENTS}
 
     expert_actions, _, _, _ = env.get_expert_actions()
 
-    env_idx = 0
-
-    highlight_agent = torch.where(control_mask[env_idx, :])[0][0].item()
-
-    for t in range(env.init_steps, env.init_steps + 10):
-        print(f"Step: {t+1}")
+    for time_step in range(env.init_steps, env.episode_len):
+        print(f"Step: {env.step_in_world[0, 0, 0].item()}")
 
         # Step the environment
         expert_actions, _, _, _ = env.get_expert_actions()
-        env.step_dynamics(expert_actions[:, :, t, :])
-
-        # Make video
-        sim_states = env.vis.plot_simulator_state(
-            env_indices=[env_idx],
-            zoom_radius=70,
-            time_steps=[t],
-            center_agent_indices=[highlight_agent],
-            plot_guidance_pos_xy=True,
-        )
-
-        agent_obs = env.vis.plot_agent_observation(
-            env_idx=env_idx,
-            agent_idx=highlight_agent,
-            figsize=(10, 10),
-            trajectory=env.reference_path[highlight_agent, :, :].to(
-                env.device
-            ),
-        )
-
-        print(env.reference_trajectory.pos_xy[control_mask].max())
-
-        sim_frames.append(img_from_fig(sim_states[0]))
-        agent_obs_frames.append(img_from_fig(agent_obs))
+        env.step_dynamics(expert_actions[:, :, time_step, :])
 
         obs = env.get_obs(control_mask)
         reward = env.get_rewards()
+        if time_step % 10 == 0 or time_step > env.episode_len - 3:
+            sim_states, agent_obs = env.render(focus_agent_idx=FOCUS_AGENTS)
+            sim_frames.append(img_from_fig(sim_states[0]))
+            for i in FOCUS_AGENTS:
+                agent_obs_frames[i].append(img_from_fig(agent_obs[i]))
 
-        print(f"A_t: {expert_actions[env_idx, highlight_agent, t, :]}")
-        print(f"R_t+1: {reward[env_idx, highlight_agent]}")
+        print(f"R_t+1: {reward[0, 0]}")
 
         done = env.get_dones()
         info = env.get_infos()
 
-        print(done[env.cont_agent_mask])
-
-        # if done.all().bool():
-        #     # Check resetting behavior
-        #     _ = env.reset(control_mask)
-        #     env.step_dynamics(expert_actions[:, :, 0, :])
-
     env.close()
 
     media.write_video(
-        "sim_video.gif", np.array(sim_frames), fps=10, codec="gif"
-    )
-    media.write_video(
-        f"obs_video_env_{env_idx}_agent_{highlight_agent}.gif",
-        np.array(agent_obs_frames),
-        fps=10,
-        codec="gif",
+        "sim_video.gif", np.array(sim_frames), fps=5, codec="gif"
     )
+    for focus_agent_idx in FOCUS_AGENTS:
+        agent_obs_frames[focus_agent_idx] = np.array(
+            agent_obs_frames[focus_agent_idx]
+        )
+        media.write_video(
+            f"obs_video_env_{0}_agent_{focus_agent_idx}.gif",
+            np.array(agent_obs_frames[focus_agent_idx]),
+            fps=5,
+            codec="gif",
+        )
diff --git a/gpudrive/integrations/puffer/ppo.py b/gpudrive/integrations/puffer/ppo.py
index ea613ae84..8459ea527 100644
--- a/gpudrive/integrations/puffer/ppo.py
+++ b/gpudrive/integrations/puffer/ppo.py
@@ -115,7 +115,11 @@ def evaluate(data):
         data.vecenv.resample_scenario_batch()
         data.resample_buffer = 0
 
-    data.vecenv.clear_render_storage()
+    # Check if there are unfinished rendered episodes
+    render_envs_to_reset = [
+        key for key, lst in data.vecenv.frames.items() if len(lst) == 0
+    ]
+    data.vecenv.clear_render_storage(env_list_to_clear=render_envs_to_reset)
 
     config, profile, experience = data.config, data.profile, data.experience
 
diff --git a/gpudrive/networks/agents.py b/gpudrive/networks/agents.py
index a6e16e295..8468d30c2 100644
--- a/gpudrive/networks/agents.py
+++ b/gpudrive/networks/agents.py
@@ -60,7 +60,6 @@ def __init__(
         if self.config["guidance"]:
             self.guidance_feature_dim = 0
             # One-hot encoding signalling the next time step
-            self.guidance_feature_dim += constants.LOG_TRAJECTORY_LENGTH
             if self.config["add_reference_pos_xy"]:
                 self.guidance_feature_dim += (
                     constants.LOG_TRAJECTORY_LENGTH * 2
diff --git a/gpudrive/utils/generate_sbatch.py b/gpudrive/utils/generate_sbatch.py
index d81078a58..b5982886a 100644
--- a/gpudrive/utils/generate_sbatch.py
+++ b/gpudrive/utils/generate_sbatch.py
@@ -243,7 +243,7 @@ def save_script(filename, file_path, fields, params, param_order=None):
 
 if __name__ == "__main__":
 
-    group = "wosac_scale_100_v3"
+    group = "wosac_scale"
 
     fields = {
         "time_h": 47,  # Max time per job (job will finish if run is done before)
@@ -258,18 +258,17 @@ def save_script(filename, file_path, fields, params, param_order=None):
         "group": [group],  # Group name
         "num_worlds": [700],
         "resample_scenes": [0],
-        "k_unique_scenes": [100],
+        "k_unique_scenes": [500],
         # "resample_interval": [5_000_000],
         # "resample_dataset_size": [10_000],
         # "total_timesteps": [3_000_000_000],
         "batch_size": [262_144],
         "minibatch_size": [16_384],
-        # "guidance_pos_xy_weight": [0.01],
         # "guidance_speed_weight": [0.01],
         # "guidance_heading_weight": [0.01],
-        "ent_coef": [0.005, 0.001, 0.003, 0.01],
+        "ent_coef": [0.001, 0.005, 0.003, 0.01],
         "vf_coef": [0.5],
-        "update_epochs": [4],
+        "update_epochs": [2, 4],
         "render": [0],
     }
 
diff --git a/gpudrive/utils/preprocess.py b/gpudrive/utils/preprocess.py
index abc324988..8069a3d11 100644
--- a/gpudrive/utils/preprocess.py
+++ b/gpudrive/utils/preprocess.py
@@ -52,8 +52,8 @@ def smooth_scenario(
         axis=-1,
     )
 
-    smoothed_vel_xy = np.zeros_like(reference_trajectory.vel_xy)
-    smoothed_yaw = np.zeros_like(reference_trajectory.yaw)
+    smoothed_vel_xy = np.zeros_like(reference_trajectory.vel_xy.cpu().numpy())
+    smoothed_yaw = np.zeros_like(reference_trajectory.yaw.cpu().numpy())
     smoothed_valid = np.zeros_like(original_valid, dtype=bool)
 
     t = np.arange(num_timesteps)
diff --git a/gpudrive/visualize/core.py b/gpudrive/visualize/core.py
index 318183787..baa5c0ebf 100644
--- a/gpudrive/visualize/core.py
+++ b/gpudrive/visualize/core.py
@@ -361,6 +361,25 @@ def plot_simulator_state(
                 center_x = 0  # Default center x-coordinate
                 center_y = 0  # Default center y-coordinate
 
+            time_step = (
+                self.env_config.episode_len
+                - self.sim_object.steps_remaining_tensor().to_torch()[
+                    env_idx, 0
+                ]
+            ).item()
+
+            # Add time step text to the figure
+            ax.text(
+                0.05,  # x position in axes coordinates (5% from left)
+                0.95,  # y position in axes coordinates (95% from bottom)
+                f"t = {time_step}",
+                transform=ax.transAxes,  # Use axes coordinates
+                fontsize=15,
+                color="black",
+                ha="left",
+                va="top",
+            )
+
             # Set zoom window around the center
             ax.set_xlim(center_x - zoom_radius, center_x + zoom_radius)
             ax.set_ylim(center_y - zoom_radius, center_y + zoom_radius)
@@ -1572,6 +1591,8 @@ def plot_agent_observation(
         env_idx: int,
         figsize: Tuple[int, int] = (10, 10),
         trajectory: Optional[np.ndarray] = None,
+        step_reward: Optional[float] = None,
+        route_progress: Optional[float] = None,
     ):
         """
         Plot observation from agent POV to inspect the information available
@@ -1809,8 +1830,8 @@ def plot_agent_observation(
         # Add time step text to the figure
         ax.text(
             0.05,  # x position in axes coordinates (5% from left)
-            0.95,  # y position in axes coordinates (95% from bottom)
-            f"t = {time_step}",
+            0.90,  # y position in axes coordinates (95% from bottom)
+             r"$O_{t}$ for " + f"t = {time_step}",
             transform=ax.transAxes,  # Use axes coordinates
             fontsize=15,
             color="black",
@@ -1818,32 +1839,59 @@ def plot_agent_observation(
             va="top",
         )
 
+        if step_reward is not None:
+            reward_color = (
+                "g" if step_reward > 0 else "r" if step_reward < 0 else "black"
+            )
+
+            ax.text(
+                0.05,  # x position in axes coordinates (5% from left)
+                0.85,  # y position in axes coordinates (90% from bottom)
+                r"$R_{t+1} = $" + f"{step_reward:.2f}",
+                transform=ax.transAxes,  # Use axes coordinates
+                fontsize=15,
+                color=reward_color,  # Using the dynamically determined color
+                ha="left",
+                va="top",
+            )
+
+        if route_progress is not None:
+            # Add route process text to the figure
+            ax.text(
+                0.05,  # x position in axes coordinates (5% from left)
+                0.80,  # y position in axes coordinates (85% from bottom)
+                f"Route progress = {route_progress:.2f}",
+                transform=ax.transAxes,  # Use axes coordinates
+                fontsize=15,
+                color="black",
+                ha="left",
+                va="top",
+            )
+
         if trajectory is not None and len(trajectory) > 0:
-            attn_reference_idx = torch.where(trajectory[:, 2] == 1)[0]
             # Plot the trajectory as a line
             ax.scatter(
-                trajectory[:, 0],  # x coordinates
-                trajectory[:, 1],  # y coordinates
+                trajectory[:, 0].cpu(),  # x coordinates
+                trajectory[:, 1].cpu(),  # y coordinates
                 color="g",
-                linewidth=0.2 * line_width_scale,
+                linewidth=0.05 * line_width_scale,
                 marker="o",
-                alpha=0.1,
+                alpha=0.3,
                 zorder=0,
             )
-            # Add a purple star above the reference position
-            if len(attn_reference_idx) > 0:
-                ref_idx = attn_reference_idx[0]
-                ref_x = trajectory[ref_idx, 0]
-                ref_y = trajectory[ref_idx, 1]
 
-                ax.scatter(
-                    ref_x,
-                    ref_y,
-                    color="#9D00FF",
-                    marker="*",
-                    s=120,
-                    zorder=10,
+            # Draw a circle around every point in the trajectory
+            for i in range(trajectory.shape[0]):
+                circle = Circle(
+                    (trajectory[i, 0].cpu(), trajectory[i, 1].cpu()),
+                    radius=self.env_config.guidance_pos_xy_radius,
+                    color="lightgrey",
+                    fill=False,
+                    linestyle="--",
+                    alpha=0.25,
                 )
+                ax.add_patch(circle)
+
 
         ax.set_xlim((-self.env_config.obs_radius, self.env_config.obs_radius))
         ax.set_ylim((-self.env_config.obs_radius, self.env_config.obs_radius))

From 4e104403da99e4b694e32c4717a4ff2c9837e159 Mon Sep 17 00:00:00 2001
From: Eugene Vinitsky <eugenevinitsky@users.noreply.github.com>
Date: Fri, 9 May 2025 16:27:22 -0400
Subject: [PATCH 09/38] model looks kinda okay? (#439)

* model looks kinda okay?

* think we added the steer angle correctly to the env

* Small bug fix

---------

Co-authored-by: Daphne Cornelisse <cor.daphne@gmail.com>
---
 gpudrive/datatypes/observation.py |  3 +++
 gpudrive/env/constants.py         |  2 +-
 gpudrive/env/env_torch.py         |  1 +
 src/dynamics.hpp                  | 38 +++++++++++++++----------------
 src/sim.cpp                       |  1 +
 src/types.hpp                     | 24 ++++++++++---------
 6 files changed, 37 insertions(+), 32 deletions(-)

diff --git a/gpudrive/datatypes/observation.py b/gpudrive/datatypes/observation.py
index e6b98b9e4..36a5bf61b 100644
--- a/gpudrive/datatypes/observation.py
+++ b/gpudrive/datatypes/observation.py
@@ -40,6 +40,7 @@ def __init__(self, self_obs_tensor: torch.Tensor, mask=None):
             self.is_collided = self_obs_tensor[:, 6]
             self.is_goal_reached = self_obs_tensor[:, 7]
             self.id = self_obs_tensor[:, 8]
+            self.steer_angle = self_obs_tensor[:, 9]
         else:
             self.speed = self_obs_tensor[:, :, 0]
             self.vehicle_length = self_obs_tensor[:, :, 1] * AGENT_SCALE
@@ -50,6 +51,7 @@ def __init__(self, self_obs_tensor: torch.Tensor, mask=None):
             self.is_collided = self_obs_tensor[:, :, 6]
             self.is_goal_reached = self_obs_tensor[:, :, 7]
             self.id = self_obs_tensor[:, :, 8]
+            self.steer_angle = self_obs_tensor[:, :, 9]
 
     @classmethod
     def from_tensor(
@@ -87,6 +89,7 @@ def normalize(self):
             min_val=constants.MIN_REL_GOAL_COORD,
             max_val=constants.MAX_REL_GOAL_COORD,
         )
+        self.steer_angle /= (torch.pi / 3)
 
     @property
     def shape(self) -> tuple[int, ...]:
diff --git a/gpudrive/env/constants.py b/gpudrive/env/constants.py
index bf5937da4..1f77b79a5 100644
--- a/gpudrive/env/constants.py
+++ b/gpudrive/env/constants.py
@@ -23,7 +23,7 @@
 MAX_ROAD_SCALE = 100
 
 # Feature shape constants
-EGO_FEAT_DIM = 6  # Ego state base fields
+EGO_FEAT_DIM = 7  # Ego state base fields
 PARTNER_FEAT_DIM = 6
 ROAD_GRAPH_FEAT_DIM = 13
 
diff --git a/gpudrive/env/env_torch.py b/gpudrive/env/env_torch.py
index 5c723c2e5..b42e24ace 100755
--- a/gpudrive/env/env_torch.py
+++ b/gpudrive/env/env_torch.py
@@ -1238,6 +1238,7 @@ def _get_ego_state(self, mask=None) -> torch.Tensor:
             ego_state.vehicle_length.unsqueeze(-1),
             ego_state.vehicle_width.unsqueeze(-1),
             ego_state.is_collided.unsqueeze(-1),
+            ego_state.steer_angle.unsqueeze(-1),
         ]
 
         if mask is None:
diff --git a/src/dynamics.hpp b/src/dynamics.hpp
index 41d98d8f2..7fba26845 100755
--- a/src/dynamics.hpp
+++ b/src/dynamics.hpp
@@ -25,28 +25,26 @@ namespace madrona_gpudrive
 
         float speed = velocity.linear.length();
         float yaw = utils::quatToYaw(rotation);
-        // Average speed
-        const float v{clipSpeed(speed + 0.5f * action.classic.acceleration * dt)};
-        const float tanDelta{tanf(action.classic.steering)};
-        // Assume center of mass lies at the middle of length, then l / L == 0.5.
-        const float beta{std::atan(0.5f * tanDelta)};
-        const math::Vector2 d{polarToVector2D(v, yaw + beta)};
-        const float w{v * std::cos(beta) * tanDelta / size.length};
-
-        // model.position += d * dt;
-        // model.heading = utils::AngleAdd(model.heading, w * dt);
-        // model.speed = clipSpeed(model.speed + action.acceleration * dt);
-        float new_yaw = utils::AngleAdd(yaw, w * dt);
-        float new_speed = clipSpeed(speed + action.classic.acceleration * dt);
-        position.x += d.x * dt;
-        position.y += d.y * dt;
-        position.z = 1;
-        rotation = Quat::angleAxis(new_yaw, madrona::math::up);
+        float x_dot = speed * cosf(utils::AngleAdd(yaw, velocity.angular.z));
+        float y_dot = speed * sinf(utils::AngleAdd(yaw, velocity.angular.z));
+        float theta_dot = speed * tanf(velocity.angular.z) / size.length;
+        float delta_dot = action.classic.steering;
+        // Update the yaw
+        float new_yaw = utils::AngleAdd(yaw, theta_dot * dt);
+        // Update the speed
+        float new_speed = speed + action.classic.acceleration * dt;
         velocity.linear.x = new_speed * cosf(new_yaw);
         velocity.linear.y = new_speed * sinf(new_yaw);
-        velocity.linear.z = 0;
-        velocity.angular = Vector3::zero();
-        velocity.angular.z = w;
+        // Update the position
+        position.x = position.x + x_dot * dt;
+        position.y = position.y + y_dot * dt;
+        // Update the rotation
+        rotation = Quat::angleAxis(new_yaw, madrona::math::up);
+        // Update the angular velocity
+        velocity.angular.z = utils::AngleAdd(velocity.angular.z, delta_dot * dt);
+        // clip angular z between -pi/3 and pi/3
+        velocity.angular.z = fmaxf(-madrona::math::pi / 3.0, fminf(velocity.angular.z, madrona::math::pi / 3.0));
+
     }
 
     inline void forwardBicycleModel(Action &action, Rotation &rotation, Position &position, Velocity &velocity)
diff --git a/src/sim.cpp b/src/sim.cpp
index 1a0e39189..fecef682e 100755
--- a/src/sim.cpp
+++ b/src/sim.cpp
@@ -193,6 +193,7 @@ inline void collectSelfObsSystem(Engine &ctx,
 
     const Info& info = ctx.get<Info>(agent_iface.e);
     self_obs.goalState = info.reachedGoal ? 1.f : 0.f;
+    self_obs.steerAngle = vel.angular.z;
 }
 
 inline void collectPartnerObsSystem(Engine &ctx,
diff --git a/src/types.hpp b/src/types.hpp
index 8d9fd25d8..3eb775e32 100755
--- a/src/types.hpp
+++ b/src/types.hpp
@@ -193,6 +193,7 @@ namespace madrona_gpudrive
         float collisionState;
         float goalState;
         float id;
+        float steerAngle;
         static inline SelfObservation zero()
         {
             return SelfObservation{
@@ -201,11 +202,12 @@ namespace madrona_gpudrive
                 .goal = {.position = {0, 0}},
                 .collisionState = 0,
                 .goalState = 0,
-                .id = -1};
+                .id = -1,
+                .steerAngle = 0};
         }
     };
 
-    const size_t SelfObservationExportSize = 9; // 1 + 3 + 2 + 1 + 1 + 1
+    const size_t SelfObservationExportSize = 10; // 1 + 3 + 2 + 1 + 1 + 1
 
     static_assert(sizeof(SelfObservation) == sizeof(float) * SelfObservationExportSize);
 
@@ -437,18 +439,18 @@ namespace madrona_gpudrive
 
     struct MetaData
     {
-        float isSdc;                
-        float isObjectOfInterest;   
-        float isTrackToPredict;     
-        float difficulty;           
+        float isSdc;
+        float isObjectOfInterest;
+        float isTrackToPredict;
+        float difficulty;
         float avgZ;
-    
+
         static inline void zero(MetaData& metadata)
         {
-            metadata.isSdc = -1.0f;           
-            metadata.isObjectOfInterest = -1.0f; 
-            metadata.isTrackToPredict = -1.0f;   
-            metadata.difficulty = -1.0f;         
+            metadata.isSdc = -1.0f;
+            metadata.isObjectOfInterest = -1.0f;
+            metadata.isTrackToPredict = -1.0f;
+            metadata.difficulty = -1.0f;
             metadata.avgZ = 0.0f;
         }
     };

From e0a32c87b3e8c2db881691c7a68dee707830b5cb Mon Sep 17 00:00:00 2001
From: Eugene Vinitsky <eugenevinitsky@users.noreply.github.com>
Date: Sat, 10 May 2025 19:59:00 -0400
Subject: [PATCH 10/38] Ev/memory decrease (#442)

* add support for model switching

* remove memory pinning

* revert changed values
---
 baselines/ppo/config/ppo_guided_autonomy.yaml | 2 +-
 baselines/ppo/ppo_guided_autonomy.py          | 2 ++
 gpudrive/integrations/puffer/ppo.py           | 4 +++-
 3 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/baselines/ppo/config/ppo_guided_autonomy.yaml b/baselines/ppo/config/ppo_guided_autonomy.yaml
index 02282cebd..3d8ebe2e0 100644
--- a/baselines/ppo/config/ppo_guided_autonomy.yaml
+++ b/baselines/ppo/config/ppo_guided_autonomy.yaml
@@ -58,7 +58,7 @@ wandb:
 train:
   exp_id: guidance_logs # Set dynamically in the script if needed
   seed: 42
-  cpu_offload: false
+  cpu_offload: true
   device: "cuda" # Dynamically set to cuda if available, else cpu
   bptt_horizon: 1
   compile: false
diff --git a/baselines/ppo/ppo_guided_autonomy.py b/baselines/ppo/ppo_guided_autonomy.py
index 53bdde822..ff279ae7e 100644
--- a/baselines/ppo/ppo_guided_autonomy.py
+++ b/baselines/ppo/ppo_guided_autonomy.py
@@ -139,6 +139,7 @@ def run(
     seed: Annotated[Optional[int], typer.Option(help="The seed for training")] = None,
     learning_rate: Annotated[Optional[float], typer.Option(help="The learning rate for training")] = None,
     anneal_lr: Annotated[Optional[int], typer.Option(help="Whether to anneal the learning rate over time; 0 or 1")] = None,
+    cpu_offload: Annotated[Optional[int], typer.Option(help="Whether to offload the buffer to cpu")] = None,
     resample_scenes: Annotated[Optional[int], typer.Option(help="Whether to resample scenes during training; 0 or 1")] = None,
     resample_interval: Annotated[Optional[int], typer.Option(help="The interval for resampling scenes")] = None,
     resample_dataset_size: Annotated[Optional[int], typer.Option(help="The size of the dataset to sample from")] = None,
@@ -224,6 +225,7 @@ def run(
         "resample_dataset_size": resample_dataset_size,
         "total_timesteps": total_timesteps,
         "ent_coef": ent_coef,
+        "cpu_offload": None if cpu_offload is None else bool(cpu_offload),
         "update_epochs": update_epochs,
         "batch_size": batch_size,
         "minibatch_size": minibatch_size,
diff --git a/gpudrive/integrations/puffer/ppo.py b/gpudrive/integrations/puffer/ppo.py
index 8459ea527..034990e3f 100644
--- a/gpudrive/integrations/puffer/ppo.py
+++ b/gpudrive/integrations/puffer/ppo.py
@@ -557,12 +557,14 @@ def __init__(
 
         obs_dtype = pufferlib.pytorch.numpy_to_torch_dtype_dict[obs_dtype]
         pin = device == "cuda" and cpu_offload
+        # TODO(ev) remove
+        pin = False
         self.obs = torch.zeros(
             batch_size,
             *obs_shape,
             dtype=obs_dtype,
             pin_memory=pin,
-            device=device if not pin else "cpu",
+            device=device if not cpu_offload else "cpu",
         )
         self.actions = torch.zeros(
             batch_size, *atn_shape, dtype=int, pin_memory=pin

From 1cad3fc1a33672877cd279c8b5b020be3d05a8af Mon Sep 17 00:00:00 2001
From: Zixu Zhang <zixu@umich.edu>
Date: Mon, 12 May 2025 12:58:25 -0400
Subject: [PATCH 11/38] update log for each scenario (#433)

---
 examples/eval/wosac_eval_origin.py | 43 +++++++++++++++++++++++++++---
 1 file changed, 40 insertions(+), 3 deletions(-)

diff --git a/examples/eval/wosac_eval_origin.py b/examples/eval/wosac_eval_origin.py
index 9bc0f57f8..c83ab0d97 100644
--- a/examples/eval/wosac_eval_origin.py
+++ b/examples/eval/wosac_eval_origin.py
@@ -19,6 +19,7 @@
     sim_agents_metrics_pb2,
     sim_agents_submission_pb2,
 )
+import csv
 
 
 class WOSACMetrics(Metric):
@@ -33,6 +34,7 @@ def __init__(
         ego_only: bool = False,
         baselines_df: Optional[pd.DataFrame] = None,
         save_table_with_baselines: bool = True,
+        log_dir: Optional[str] = None,
     ) -> None:
         super().__init__()
         self.is_mp_init = False
@@ -53,7 +55,7 @@ def __init__(
         else:
             self.baselines_df = baselines_df
 
-        self.timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H")
+        self.timestamp = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
 
         self.field_names = [
             "metametric",
@@ -79,6 +81,19 @@ def __init__(
         )
         tf.config.set_visible_devices([], "GPU")
 
+        if log_dir is not None:
+            # Get data and time for the current run as filename
+            log_dir = Path(log_dir)
+            self.log_file_path = log_dir / f"{self.timestamp}.csv"
+            log_dir.mkdir(parents=True, exist_ok=True)
+            self.log_file = csv.writer(open(self.log_file_path, 'w'))
+            # Add header to the log file
+            self.log_file.writerow(self.field_names) 
+
+
+        else:
+            self.log_file = None
+
     def _create_baselines_df(self) -> pd.DataFrame:
         """Create a DataFrame with baseline metrics based on the provided table."""
         columns = [
@@ -212,15 +227,37 @@ def update(
             scenario_files, scenario_rollouts, scenario_rollout_masks
         ):
             try:
-                pool_scenario_metrics.append(
-                    self._compute_scenario_metrics(
+                scenario_result = self._compute_scenario_metrics(
                         self.wosac_config,
                         _scenario,
                         _scenario_rollout,
                         self.ego_only,
                         # scenario_rollouts_mask=_scenario_mask
                     )
+                pool_scenario_metrics.append(
+                    scenario_result
                 )
+
+                if self.log_file is not None:
+                    # Log the scenario metrics to the file  
+                    result_to_log = [
+                        scenario_result.metametric,
+                        scenario_result.average_displacement_error
+                        scenario_result.linear_speed_likelihood,
+                        scenario_result.linear_acceleration_likelihood,
+                        scenario_result.angular_speed_likelihood,
+                        scenario_result.angular_acceleration_likelihood,
+                        scenario_result.distance_to_nearest_object_likelihood,
+                        scenario_result.collision_indication_likelihood,
+                        scenario_result.time_to_collision_likelihood,
+                        scenario_result.distance_to_road_edge_likelihood,
+                        scenario_result.offroad_indication_likelihood,
+                        scenario_result.min_average_displacement_error,
+                        scenario_result.traffic_light_violation_likelihood,
+                        scenario_result.simulated_collision_rate,
+                        scenario_result.simulated_offroad_rate, 
+                    ]
+                    self.log_file.writerow(result_to_log)
             except Exception as e:
                 print(
                     f"Error processing scenario {_scenario_rollout.scenario_id}"

From e99ec5431df0372dd86ef6de032c8fae88832bd8 Mon Sep 17 00:00:00 2001
From: Daphne Cornelisse <33460159+daphne-cornelisse@users.noreply.github.com>
Date: Mon, 12 May 2025 15:15:00 -0400
Subject: [PATCH 12/38] Bug fixes to get rid of large position values with
 `vbd_online` (#435)

* Bug fix: Ensure there is always a warmup period of at least 10 steps when using vbd_online

* online guidance fixes

* minor fix

* changes to get 91 steps

* Keep episodeLen at 90, and don't use it toread in the logs

* Add back warnings / idiot proofing

* tiny fix

* fix online guidance

* Set max agents to 32

---------

Co-authored-by: kevin <kevinwinston184@gmail.com>
---
 ...rogress__S_1__05_04_17_37_18_741_001677.pt |     1 -
 .../notebooks/02_guidance_data_analysis.ipynb | 13364 ++++++++--------
 .../eval/notebooks/obtain_guidance_data.py    |     8 +-
 gpudrive/datatypes/trajectory.py              |    16 +-
 gpudrive/env/base_env.py                      |    10 +-
 gpudrive/env/config.py                        |     2 +-
 gpudrive/env/env_torch.py                     |    78 +-
 gpudrive/integrations/vbd/data/amortize.py    |    33 +-
 src/bindings.cpp                              |     1 +
 src/consts.hpp                                |     2 +-
 src/json_serialization.hpp                    |     7 +-
 src/types.hpp                                 |    10 +-
 12 files changed, 6843 insertions(+), 6689 deletions(-)
 delete mode 120000 checkpoints/model_guidance_progress__S_1__05_04_17_37_18_741_001677.pt

diff --git a/checkpoints/model_guidance_progress__S_1__05_04_17_37_18_741_001677.pt b/checkpoints/model_guidance_progress__S_1__05_04_17_37_18_741_001677.pt
deleted file mode 120000
index e6ab06eb1..000000000
--- a/checkpoints/model_guidance_progress__S_1__05_04_17_37_18_741_001677.pt
+++ /dev/null
@@ -1 +0,0 @@
-/home/emerge/gpudrive/runs/guidance_progress__S_1__05_04_17_37_18_741/model_guidance_progress__S_1__05_04_17_37_18_741_001677.pt
\ No newline at end of file
diff --git a/examples/eval/notebooks/02_guidance_data_analysis.ipynb b/examples/eval/notebooks/02_guidance_data_analysis.ipynb
index 7b91ec50f..19af0e2ec 100644
--- a/examples/eval/notebooks/02_guidance_data_analysis.ipynb
+++ b/examples/eval/notebooks/02_guidance_data_analysis.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -134,22 +134,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([4, 64, 91, 2])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "env.reference_trajectory.pos_xy[0, 0, :, 0]"
+    "#env.reference_trajectory.pos_xy[0, 0, :, 0]"
    ]
   },
   {
@@ -161,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -171,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -205,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -219,7 +208,7 @@
        "  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n",
        "   <cc:Work>\n",
        "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
-       "    <dc:date>2025-05-02T11:58:00.806526</dc:date>\n",
+       "    <dc:date>2025-05-02T12:34:22.763205</dc:date>\n",
        "    <dc:format>image/svg+xml</dc:format>\n",
        "    <dc:creator>\n",
        "     <cc:Agent>\n",
@@ -256,12 +245,12 @@
        "    <g id=\"xtick_1\">\n",
        "     <g id=\"line2d_1\">\n",
        "      <defs>\n",
-       "       <path id=\"m3471ea14a0\" d=\"M 0 0 \n",
+       "       <path id=\"m065e802b8c\" d=\"M 0 0 \n",
        "L 0 6 \n",
        "\" style=\"stroke: #262626; stroke-width: 1.25\"/>\n",
        "      </defs>\n",
        "      <g>\n",
-       "       <use xlink:href=\"#m3471ea14a0\" x=\"69.629406\" y=\"158.218031\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
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@@ -2350,13 +2339,13 @@
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@@ -2419,12 +2408,12 @@
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@@ -2493,7 +2482,7 @@
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@@ -2520,7 +2509,7 @@
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@@ -2892,7 +2881,7 @@
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@@ -3375,7 +3364,7 @@
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@@ -3656,7 +3645,7 @@
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@@ -3762,7 +3751,7 @@
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@@ -3775,7 +3764,7 @@
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@@ -3803,7 +3792,7 @@
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@@ -3831,7 +3820,7 @@
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@@ -3847,7 +3836,7 @@
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@@ -4111,7 +4100,7 @@
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@@ -4326,13 +4315,13 @@
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@@ -4358,7 +4347,7 @@
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-       "    <dc:date>2025-05-02T11:58:01.175975</dc:date>\n",
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@@ -4395,12 +4384,12 @@
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        "      <defs>\n",
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@@ -4436,7 +4425,7 @@
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@@ -4466,7 +4455,7 @@
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@@ -4531,12 +4520,12 @@
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@@ -9515,7 +9504,7 @@
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@@ -9531,7 +9520,7 @@
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        "   </g>\n",
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@@ -9628,7 +9617,7 @@
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@@ -9843,13 +9832,13 @@
        "  </g>\n",
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        " <defs>\n",
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@@ -9889,7 +9878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -9903,16 +9892,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "((194, 91, 6), (1180, 90, 6), (1180, 80, 6))"
+       "((1180, 91, 6), (1180, 90, 6), (38, 80, 6))"
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-     "execution_count": 21,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
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@@ -9931,7 +9920,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
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        "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
-       "    <dc:date>2025-05-02T09:58:44.004711</dc:date>\n",
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@@ -9982,12 +9971,12 @@
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        "      <defs>\n",
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@@ -10057,7 +10046,7 @@
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@@ -10102,7 +10091,7 @@
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@@ -10117,7 +10106,7 @@
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@@ -10579,12 +10568,12 @@
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        "      <defs>\n",
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@@ -10597,7 +10586,7 @@
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@@ -10614,7 +10603,7 @@
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@@ -10652,7 +10641,7 @@
        "    <g id=\"ytick_4\">\n",
        "     <g id=\"line2d_9\">\n",
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@@ -10701,7 +10690,7 @@
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@@ -10759,7 +10748,7 @@
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@@ -10836,7 +10825,7 @@
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@@ -10844,7 +10833,7 @@
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@@ -10852,7 +10841,7 @@
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@@ -10860,7 +10849,7 @@
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@@ -10868,7 +10857,7 @@
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@@ -10876,7 +10865,7 @@
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@@ -10884,7 +10873,7 @@
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@@ -10892,7 +10881,7 @@
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@@ -10900,7 +10889,7 @@
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@@ -10908,7 +10897,7 @@
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@@ -10916,7 +10905,7 @@
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@@ -10940,7 +10929,7 @@
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@@ -10948,7 +10937,7 @@
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@@ -11755,7 +11788,7 @@
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@@ -11804,7 +11837,7 @@
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-       "   <g id=\"text_35\">\n",
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@@ -11855,7 +11888,7 @@
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-       "   <g id=\"text_36\">\n",
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@@ -11873,13 +11906,13 @@
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@@ -11918,7 +11951,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
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-       "    <dc:date>2025-05-02T09:58:49.734324</dc:date>\n",
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@@ -11969,12 +12002,12 @@
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@@ -12475,12 +12508,12 @@
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@@ -12493,7 +12526,7 @@
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@@ -12537,7 +12570,7 @@
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@@ -12571,7 +12604,7 @@
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+   "execution_count": 50,
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        "      <defs>\n",
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+       "       <use xlink:href=\"#m5f822aa111\" x=\"114.574714\" y=\"193.665\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
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+       "       <use xlink:href=\"#m5f822aa111\" x=\"176.314813\" y=\"193.665\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
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   {
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+   "execution_count": 51,
    "metadata": {},
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        "      <defs>\n",
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@@ -38462,8 +38545,7 @@
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@@ -38556,7 +38638,7 @@
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        "   <g id=\"patch_8\">\n",
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@@ -38662,7 +38744,7 @@
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        "      <g>\n",
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@@ -38675,7 +38757,7 @@
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@@ -38689,7 +38771,7 @@
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@@ -38703,7 +38785,7 @@
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@@ -38767,12 +38849,12 @@
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        "L 820.125685 32.203304 \n",
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        "   </g>\n",
        "   <g id=\"patch_13\">\n",
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@@ -39393,13 +39475,13 @@
        "  </g>\n",
        " </g>\n",
        " <defs>\n",
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        "  </clipPath>\n",
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        "  </clipPath>\n",
        " </defs>\n",
diff --git a/examples/eval/notebooks/obtain_guidance_data.py b/examples/eval/notebooks/obtain_guidance_data.py
index 5184c42a7..59ed9981d 100644
--- a/examples/eval/notebooks/obtain_guidance_data.py
+++ b/examples/eval/notebooks/obtain_guidance_data.py
@@ -11,7 +11,7 @@
 
 if __name__ == "__main__":
 
-    GUIDANCE_MODE = "log_replay"
+    GUIDANCE_MODE = "vbd_online"
     DATASET = "data/processed/wosac/validation_json_100"  # Ensure VBD trajectory structures are in here
     SAVE_PATH = "examples/eval/figures_data/"
 
@@ -24,13 +24,13 @@
         add_reference_speed=True,
         add_reference_pos_xy=True,
         init_mode="wosac_train",
-        smoothen_trajectory=True,
+        smoothen_trajectory=False,
     )
     render_config = RenderConfig()
 
     train_loader = SceneDataLoader(
         root=DATASET,
-        batch_size=10,
+        batch_size=2,
         dataset_size=100,
         sample_with_replacement=False,
         shuffle=False,
@@ -61,6 +61,6 @@
     )
 
     np.save(
-        f"{SAVE_PATH}reference_{GUIDANCE_MODE}_smooth.npy", reference_traj_np
+        f"{SAVE_PATH}reference_{GUIDANCE_MODE}.npy", reference_traj_np
     )
     print(f"Saved reference trajectory for {GUIDANCE_MODE} mode.")
diff --git a/gpudrive/datatypes/trajectory.py b/gpudrive/datatypes/trajectory.py
index eba306510..f36dc5306 100644
--- a/gpudrive/datatypes/trajectory.py
+++ b/gpudrive/datatypes/trajectory.py
@@ -151,14 +151,14 @@ def __init__(
         self.vel_xy = vbd_traj_tensor[:, :, :, 3:5]
         self.ref_speed = self.comp_reference_speed()
         # Assumption: All timesteps are valid (correct)
-        self.valids = torch.ones_like(self.pos_x, dtype=torch.int32)
+        self.valids = vbd_traj_tensor[:, :, :, 5].unsqueeze(-1).to(
+            torch.int32
+        )
 
         self.mean_pos_xy = mean_pos_xy
         self.mean_x = mean_pos_xy[:, 0]
         self.mean_y = mean_pos_xy[:, 1]
 
-        self.demean_positions()
-
     @classmethod
     def from_tensor(
         cls,
@@ -195,8 +195,8 @@ def demean_positions(self):
         mean_y_reshaped = self.mean_y.view(-1, 1, 1)
 
         # Apply to x and y coordinates
-        self.pos_xy[..., 0] -= mean_x_reshaped
-        self.pos_xy[..., 1] -= mean_y_reshaped
+        self.pos_xy[..., 10:, 0] -= mean_x_reshaped
+        self.pos_xy[..., 10:, 1] -= mean_y_reshaped
 
         self.pos_x = self.pos_xy[..., 0].unsqueeze(-1)
         self.pos_y = self.pos_xy[..., 1].unsqueeze(-1)
@@ -224,9 +224,9 @@ def __init__(self, vbd_traj_tensor: torch.Tensor):
         self.vel_y = vbd_traj_tensor[:, :, :, 4].unsqueeze(-1)
         self.vel_xy = vbd_traj_tensor[:, :, :, 3:5]
         self.ref_speed = self.comp_reference_speed()
-        # Assumption: All timesteps are valid
-        # TODO: The first 10 timesteps come from the logs, so there may be invalid steps
-        self.valids = torch.ones_like(self.pos_x, dtype=torch.int32)
+        self.valids = vbd_traj_tensor[:, :, :, 5].unsqueeze(-1).to(
+            torch.int32
+        )
 
     @classmethod
     def from_tensor(
diff --git a/gpudrive/env/base_env.py b/gpudrive/env/base_env.py
index cb1258238..cb99f21ff 100755
--- a/gpudrive/env/base_env.py
+++ b/gpudrive/env/base_env.py
@@ -165,7 +165,15 @@ def _setup_environment_parameters(self):
         params = self._set_collision_behavior(params)
         params = self._set_road_reduction_params(params)
         params = self._set_goal_behavior(params)
-        self.init_steps = getattr(self.config, "init_steps", 0)
+    
+        if self.config.guidance and self.config.guidance_mode == "vbd_online":
+            self.init_steps = max(self.config.init_steps, 10)
+            print(
+                f"\n[Note] Guidance mode '{self.config.guidance_mode}' requires at least 10 initialization steps to provide sufficient scene context for the diffusion model. Automatically setting simulator time to t = {self.init_steps}. \n"
+            )
+        else:
+            self.init_steps = getattr(self.config, "init_steps", 0)
+            
         params.initSteps = self.init_steps
 
         return params
diff --git a/gpudrive/env/config.py b/gpudrive/env/config.py
index 29721cf44..960128c29 100755
--- a/gpudrive/env/config.py
+++ b/gpudrive/env/config.py
@@ -40,7 +40,7 @@ class EnvConfig:
     add_reference_pos_xy: bool = True  # (x, y) position time series
     add_reference_speed: bool = False  # speed time series
     add_reference_heading: bool = False  # heading time series
-    smoothen_trajectory: bool = True  # Filters out the trajectory
+    smoothen_trajectory: bool = False  # Filters out the trajectory
     guidance_pos_xy_radius: float = 1.0  # Tightness of the positions guidance
 
     # Maximum number of controlled agents in the scene
diff --git a/gpudrive/env/env_torch.py b/gpudrive/env/env_torch.py
index b42e24ace..cdb0fe3cf 100755
--- a/gpudrive/env/env_torch.py
+++ b/gpudrive/env/env_torch.py
@@ -36,6 +36,7 @@
 from gpudrive.integrations.vbd.data.utils import process_scenario_data
 
 from gpudrive.utils.preprocess import smooth_scenario
+import madrona_gpudrive
 
 
 class GPUDriveTorchEnv(GPUDriveGymEnv):
@@ -72,18 +73,16 @@ def __init__(
 
         # Get the initial data batch (set of traffic scenarios)
         self.data_batch = next(self.data_iterator)
-
+        
         assert self.num_worlds == len(
             self.data_batch
         ), f"Number of scenarios in data_batch ({len(self.data_batch)}) \
         should equal number of worlds ({self.num_worlds}). \
         \n Please check your data loader configuration."
-
+        
         # Initialize simulator
         self.sim = self._initialize_simulator(params, self.data_batch)
 
-        self.init_steps = self.config.init_steps
-
         # Controlled agents setup
         self.cont_agent_mask = self.get_controlled_agents_mask()
         self.max_agent_count = self.cont_agent_mask.shape[1]
@@ -157,16 +156,16 @@ def setup_guidance(self):
                 trajectory_tensor, self.backend, self.device
             )
         elif self.guidance_mode == "vbd_online":
-
-            self.init_steps = max(self.init_steps, 10)
-            print(
-                f"\n[Note] Guidance mode '{self.guidance_mode}' requires at least {self.init_steps} initialization steps to provide sufficient scene context for the diffusion model. Automatically setting simulator time to t = {self.init_steps}. \n"
-            )
-
             # Load pre-trained Versatile Behavior Diffusion (VBD) model
             self.vbd_model = self._load_vbd_model(
                 model_path=self.config.vbd_model_path
             )
+            
+            self.init_steps = max(self.init_steps, 10)
+            print(
+                f"\n[Note] Guidance mode '{self.guidance_mode}' requires at least {self.init_steps} initialization steps to provide sufficient scene context for the diffusion model. Automatically setting simulator time to t = {self.init_steps}. \n"
+            )
+            
             # Construct scene context dict for the VBD model
             scene_context = self.construct_context(init_steps=self.init_steps)
 
@@ -175,9 +174,66 @@ def setup_guidance(self):
             # Query the model online for the reference trajectory
             predicted = self.vbd_model.sample_denoiser(scene_context)
 
+            # Pad first 10 steps with logs
+            trajectory_tensor = self.sim.expert_trajectory_tensor()
+            log_trajectory = LogTrajectory.from_tensor(
+                trajectory_tensor,
+                self.num_worlds,
+                self.max_agent_count,
+                self.backend,
+                self.device,
+            )
+            
+            reference_trajectory = torch.zeros(
+                self.num_worlds,
+                self.max_agent_count,
+                madrona_gpudrive.kTrajectoryLength,
+                6
+            )
+            reference_trajectory[:, :, :self.init_steps + 1, :2] = log_trajectory.pos_xy[
+                :, :, :self.init_steps + 1
+            ]
+            reference_trajectory[:, :, :self.init_steps + 1, 2] = log_trajectory.yaw[
+                :, :, :self.init_steps + 1, 0
+            ]
+            reference_trajectory[:, :, :self.init_steps + 1, 3:5] = log_trajectory.vel_xy[
+                :, :, :self.init_steps + 1
+            ]
+            reference_trajectory[:, :, :self.init_steps + 1, 5] = log_trajectory.valids[
+                :, :, :self.init_steps + 1, 0
+            ]
+
+            vbd_predictions=predicted["denoised_trajs"].to(self.device).detach()
+
+            # Get the world means
+            world_means = (
+                self.sim.world_means_tensor().to_torch()[:, :2].to(self.device)
+            )
+
+            # Add vbd predictions to the reference trajectory
+            for i in range(self.num_worlds):
+                # Get controlled agent indices for this world
+                valid_mask = (
+                    scene_context["agents_id"][i] >= 0
+                )
+                valid_world_indices = scene_context["agents_id"][i][valid_mask]
+
+                reference_trajectory[
+                    i, valid_world_indices, self.init_steps + 1:, :2
+                ] = vbd_predictions[i, valid_world_indices, :, :2] - world_means[
+                    i
+                ].view(1, 1, 2)
+                reference_trajectory[
+                    i, valid_world_indices, self.init_steps + 1:, 2:5
+                ] = vbd_predictions[i, valid_world_indices, :, 2:5]
+                reference_trajectory[
+                    i, valid_world_indices, self.init_steps + 1:, 5
+                ] = 1
+
+            
             # Wrap predictions into a VBDTrajectoryOnline object
             self.reference_trajectory = VBDTrajectoryOnline.from_tensor(
-                vbd_predictions=predicted["denoised_trajs"],
+                vbd_predictions=reference_trajectory,
                 mean_pos_xy=self.sim.world_means_tensor().to_torch()[:, :2],
                 backend=self.backend,
                 device=self.device,
diff --git a/gpudrive/integrations/vbd/data/amortize.py b/gpudrive/integrations/vbd/data/amortize.py
index 394501adc..3f91ec946 100644
--- a/gpudrive/integrations/vbd/data/amortize.py
+++ b/gpudrive/integrations/vbd/data/amortize.py
@@ -27,6 +27,7 @@
 from gpudrive.datatypes.trajectory import LogTrajectory
 from gpudrive.datatypes.observation import GlobalEgoState
 from gpudrive.integrations.vbd.sim_agent.sim_actor import VBDTest
+import madrona_gpudrive
 
 
 def load_vbd_model(model_path, device="cpu", max_cont_agents=64):
@@ -77,7 +78,7 @@ def main():
 
     # Always use CPU device
     device = "cpu"
-    MAX_CONTROLLED_AGENTS = 64
+    MAX_CONTROLLED_AGENTS = 32
     print(f"Loading VBD model on {device}...")
     vbd_model = load_vbd_model(args.model_path, device, MAX_CONTROLLED_AGENTS)
 
@@ -106,7 +107,7 @@ def main():
             dataset_size=args.num_scenes,
             file_prefix="",
         ),
-        render_config=RenderConfig(render_3d=True),
+        render_config=RenderConfig(),
         max_cont_agents=MAX_CONTROLLED_AGENTS,  # Maximum number of agents to control per scene
         device=device,
     )
@@ -115,8 +116,8 @@ def main():
     output_trajectories = torch.zeros(
         gpudrive_env.num_worlds,
         gpudrive_env.max_agent_count,
-        env_config.episode_len,
-        5,
+        madrona_gpudrive.kTrajectoryLength,
+        6,
     )
 
     # Save init steps from logs
@@ -127,14 +128,17 @@ def main():
         device=device,
     )
 
-    output_trajectories[:, :, :INIT_STEPS, :2] = log_trajectory.pos_xy[
-        :, :, :INIT_STEPS
+    output_trajectories[:, :, : INIT_STEPS + 1, :2] = log_trajectory.pos_xy[
+        :, :, : INIT_STEPS + 1
     ]
-    output_trajectories[:, :, :INIT_STEPS, 2] = log_trajectory.yaw[
-        :, :, :INIT_STEPS, 0
+    output_trajectories[:, :, : INIT_STEPS + 1, 2] = log_trajectory.yaw[
+        :, :, : INIT_STEPS + 1, 0
     ]
-    output_trajectories[:, :, :INIT_STEPS, 3:] = log_trajectory.vel_xy[
-        :, :, :INIT_STEPS
+    output_trajectories[:, :, : INIT_STEPS + 1, 3:5] = log_trajectory.vel_xy[
+        :, :, : INIT_STEPS + 1
+    ]
+    output_trajectories[:, :, : INIT_STEPS + 1, 5] = log_trajectory.valids[
+        :, :, : INIT_STEPS + 1, 0
     ]
 
     # Action tensor to step through simulation
@@ -181,20 +185,23 @@ def main():
                 i, valid_world_indices, :, 2
             ]
             predicted_actions[i, valid_world_indices, :, 4:6] = vbd_output[
-                i, valid_world_indices, :, 3:
+                i, valid_world_indices, :, 3:5
             ]
 
             # Populate output trajectories
             output_trajectories[
-                i, valid_world_indices, INIT_STEPS:, :2
+                i, valid_world_indices, INIT_STEPS + 1 :, :2
             ] = vbd_output[i, valid_world_indices, :, :2] - world_means[
                 i
             ].view(
                 1, 1, 2
             )
             output_trajectories[
-                i, valid_world_indices, INIT_STEPS:, 2:
+                i, valid_world_indices, INIT_STEPS + 1 :, 2:5
             ] = vbd_output[i, valid_world_indices, :, 2:]
+            output_trajectories[
+                i, valid_world_indices, INIT_STEPS + 1 :, 5
+            ] = 1.0
 
         # Save to each file's json
         index_to_id = GlobalEgoState.from_tensor(
diff --git a/src/bindings.cpp b/src/bindings.cpp
index e3edfe6cd..d725606b3 100755
--- a/src/bindings.cpp
+++ b/src/bindings.cpp
@@ -23,6 +23,7 @@ namespace madrona_gpudrive
         m.attr("kMaxAgentCount") = consts::kMaxAgentCount;
         m.attr("kMaxRoadEntityCount") = consts::kMaxRoadEntityCount;
         m.attr("kMaxAgentMapObservationsCount") = consts::kMaxAgentMapObservationsCount;
+        m.attr("kTrajectoryLength") = consts::kTrajectoryLength;
         m.attr("episodeLen") = consts::episodeLen;
         m.attr("numLidarSamples") = consts::numLidarSamples;
         m.attr("vehicleScale") = consts::vehicleLengthScale;
diff --git a/src/consts.hpp b/src/consts.hpp
index d5e76b834..19200f798 100644
--- a/src/consts.hpp
+++ b/src/consts.hpp
@@ -8,7 +8,7 @@ namespace madrona_gpudrive {
 
 namespace consts {
 
-inline constexpr madrona::CountT kMaxAgentCount = 64;
+inline constexpr madrona::CountT kMaxAgentCount = 32;
 inline constexpr madrona::CountT kMaxRoadEntityCount = 10000;
 inline constexpr madrona::CountT kMaxAgentMapObservationsCount = 128;
 
diff --git a/src/json_serialization.hpp b/src/json_serialization.hpp
index 20c61828a..73ad38551 100644
--- a/src/json_serialization.hpp
+++ b/src/json_serialization.hpp
@@ -145,8 +145,8 @@ namespace madrona_gpudrive
         obj.metadata.avgZ = avgZ;
 
         // Initialize VBD trajectories to zeros
-        for (int i = 0; i < consts::episodeLen; i++) {
-            for (int j = 0; j < 5; j++) {
+        for (int i = 0; i < consts::kTrajectoryLength; i++) {
+            for (int j = 0; j < 6; j++) {
                 obj.vbd_trajectories[i][j] = 0.0f;
             }
         }
@@ -155,13 +155,14 @@ namespace madrona_gpudrive
         if (j.contains("vbd_trajectory")) {
             int vbd_idx = 0;
             for (const auto &vbd_traj : j.at("vbd_trajectory")) {
-                if (vbd_idx < consts::episodeLen) {
+                if (vbd_idx < consts::kTrajectoryLength) {
                     if (!vbd_traj.is_null()) {
                         obj.vbd_trajectories[vbd_idx][0] = vbd_traj.at(0).get<float>();
                         obj.vbd_trajectories[vbd_idx][1] = vbd_traj.at(1).get<float>();
                         obj.vbd_trajectories[vbd_idx][2] = vbd_traj.at(2).get<float>();
                         obj.vbd_trajectories[vbd_idx][3] = vbd_traj.at(3).get<float>();
                         obj.vbd_trajectories[vbd_idx][4] = vbd_traj.at(4).get<float>();
+                        obj.vbd_trajectories[vbd_idx][5] = vbd_traj.at(5).get<float>();
                     }
                 }
                 vbd_idx++;
diff --git a/src/types.hpp b/src/types.hpp
index 3eb775e32..b0fe159ab 100755
--- a/src/types.hpp
+++ b/src/types.hpp
@@ -366,15 +366,15 @@ namespace madrona_gpudrive
 
     struct VBDTrajectory
     {
-        // For each agent, store the full VBD trajectory (x, y, yaw, vx, vy)
-        // The tensor has shape [traj_length, 5]
-        float trajectories[consts::episodeLen][5];
+        // For each agent, store the full VBD trajectory (x, y, yaw, vx, vy, valid)
+        // The tensor has shape [traj_length, 6]
+        float trajectories[consts::episodeLen][6];
 
         static inline void zero(VBDTrajectory& vbd_traj)
         {
             for (int i = 0; i < consts::episodeLen; i++)
             {
-                for (int j = 0; j < 5; j++)
+                for (int j = 0; j < 6; j++)
                 {
                     vbd_traj.trajectories[i][j] = 0.0f;
                 }
@@ -382,7 +382,7 @@ namespace madrona_gpudrive
         }
     };
 
-    const size_t VBDTrajectoryExportSize = consts::episodeLen * 5;
+    const size_t VBDTrajectoryExportSize = consts::episodeLen * 6;
 
     static_assert(sizeof(VBDTrajectory) == sizeof(float) * VBDTrajectoryExportSize);
 

From 5fa3c37c069b8cd08fa2b41be938f7f74b3c7f2f Mon Sep 17 00:00:00 2001
From: Daphne Cornelisse <33460159+daphne-cornelisse@users.noreply.github.com>
Date: Tue, 13 May 2025 11:46:28 -0400
Subject: [PATCH 13/38] RL training improvements (#440)

* Small improvements

* Minor training improvements

* Rebase over dev

* Bookkeeping

* Bookkeeping

* Small fixes for gpu

* wip: config

* Make adding the action optional

* wip

* Analysis notebooks

* WIP: new reward mode

* Frequency

* Make sure to normalize rewards and other changes

* Minor

* Update model

* Add bonus at end of episode mechanism

* Fixes

* Improve renderer

* Rmv one-hot encoding

* Fix render

* Adding back the whole shazam

* Naming stuff

* Add data folder as cmdline arg

* Add data folder as cmdline arg

* Minor bug fixes

* Cleanup and better error message

* Align reward components scale

* Push models

* Viz improvements

* Data analysis

* Add guidance dropout mask

* Add dropout as option for training

* Add dropout as option for training

* Bug fix: only count valid points

* Increase network size to 200K params

* Bug fix

* Larger net

* Set controlled agent default to 32

* Minor improvements

* WIP

* Make agents their original size

* Minor

* Remove collision state from ego

* Plotting stuff

* wip

* More plotting stuff

* Minor

* Fixes

* Reward improvements

* Reward improvements

* Give agent a bit more road graph information

* Visualizer bug fix: clean up axis before generating new plot

* Formatting

* Change dropout mechanism such that dguidance_dropout_prob represents the maximum dropout probability [cover wide range].

* Add mechanism to discourage agents from turning around for bonus

* Fix sbatch generation script

* Add speed penalty if end of traj is reached

* wip

* Some eval fixes

* Eval wip

* Fix action space bug

* Fix defaults

* VBD amortize shape bug fix

* rmv speed penalty

* Settings

* Fig

* huh?

* Replace end of route bonus with small jerk penalty

* Many improvements

* Exclude data

* Delete checkpoints

* Minor
---
 .gitignore                                    |    2 +
 baselines/ppo/config/ppo_guided_autonomy.yaml |   41 +-
 baselines/ppo/ppo_guided_autonomy.py          |   22 +
 .../figures_data/reference_log_replay.npy     |  Bin 2577248 -> 0 bytes
 .../figures_data/reference_log_replay_raw.npy |  Bin 423824 -> 0 bytes
 .../reference_log_replay_smooth.npy           |  Bin 423824 -> 0 bytes
 .../figures_data/reference_vbd_amortized.npy  |  Bin 2548928 -> 0 bytes
 .../figures_data/reference_vbd_online.npy     |  Bin 2265728 -> 0 bytes
 .../eval/notebooks/01_wosac_results.ipynb     | 3001 +++++++-
 .../notebooks/02_guidance_data_analysis.ipynb | 6607 ++++++++++++++++-
 .../eval/notebooks/obtain_guidance_data.py    |    4 +-
 .../notebooks/variability_in_dataset.ipynb    | 1716 +++++
 examples/eval/rollout_test.py                 |  116 +-
 examples/eval/run_wosac_eval.py               |  107 +-
 examples/eval/wosac_eval_origin.py            |   16 +-
 examples/experimental/notebooks/debug.ipynb   | 6382 +---------------
 gpudrive/datatypes/trajectory.py              |   22 +-
 gpudrive/env/config.py                        |   19 +-
 gpudrive/env/constants.py                     |    2 +-
 gpudrive/env/env_puffer.py                    |   24 +-
 gpudrive/env/env_torch.py                     |  276 +-
 gpudrive/integrations/puffer/ppo.py           |    2 -
 gpudrive/networks/agents.py                   |   21 +-
 gpudrive/utils/generate_sbatch.py             |   87 +-
 gpudrive/visualize/color.py                   |    6 +-
 gpudrive/visualize/core.py                    |  280 +-
 gpudrive/visualize/utils.py                   |    2 +-
 src/consts.hpp                                |    2 +-
 src/mgr.cpp                                   |    2 +-
 29 files changed, 12041 insertions(+), 6718 deletions(-)
 delete mode 100644 examples/eval/figures_data/reference_log_replay.npy
 delete mode 100644 examples/eval/figures_data/reference_log_replay_raw.npy
 delete mode 100644 examples/eval/figures_data/reference_log_replay_smooth.npy
 delete mode 100644 examples/eval/figures_data/reference_vbd_amortized.npy
 delete mode 100644 examples/eval/figures_data/reference_vbd_online.npy
 create mode 100644 examples/eval/notebooks/variability_in_dataset.ipynb

diff --git a/.gitignore b/.gitignore
index 0c70e5cd2..eed9b1fe7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -30,6 +30,8 @@ data/processed/testing/*
 data/processed/pop_play/*
 data/processed/hand_designed/*
 figures/
+checkpoints/
+figures_data/
 data/other/*
 wosac/
 
diff --git a/baselines/ppo/config/ppo_guided_autonomy.yaml b/baselines/ppo/config/ppo_guided_autonomy.yaml
index 3d8ebe2e0..985302f7c 100644
--- a/baselines/ppo/config/ppo_guided_autonomy.yaml
+++ b/baselines/ppo/config/ppo_guided_autonomy.yaml
@@ -8,9 +8,9 @@ model_cpt: null
 
 environment: # Overrides default environment configs (see pygpudrive/env/config.py)
   name: "gpudrive"
-  num_worlds: 100 # Number of parallel environments
+  num_worlds: 10 # Number of parallel environments
   k_unique_scenes: 1 # Number of unique scenes to sample from
-  max_controlled_agents: 64 # Maximum number of agents controlled by the model. Make sure this aligns with the variable kMaxAgentCount in src/consts.hpp
+  max_controlled_agents: 32 # Maximum number of agents controlled by the model. Make sure this aligns with the variable kMaxAgentCount in src/consts.hpp
   ego_state: true
   road_map_obs: true
   partner_obs: true
@@ -24,14 +24,15 @@ environment: # Overrides default environment configs (see pygpudrive/env/config.
   add_reference_speed: true # If true, the reference speeds are added to the ego observation
   add_reference_heading: true # If true, the reference heading are added to the ego observation
   smoothen_trajectory: true # If true, the velocities and headings are smoothed
+  guidance_dropout_prob: 0.0 # Probability of out guidance points
 
   # Reward function
   reward_type: "guided_autonomy"
-  collision_weight: -0.01 
-  off_road_weight: -0.01
-  guidance_speed_weight: 0.01
-  guidance_heading_weight: 0.01
-  smoothness_weight: 0.0001
+  collision_weight: -0.01
+  off_road_weight: -0.01 
+  guidance_speed_weight: 0.005
+  guidance_heading_weight: 0.005
+  smoothness_weight: 0.0
 
   init_mode: wosac_train
   dynamics_model: "classic"
@@ -41,9 +42,9 @@ environment: # Overrides default environment configs (see pygpudrive/env/config.
   polyline_reduction_threshold: 0.1 # Rate at which to sample points from the polyline (0 is use all closest points, 1 maximum sparsity), needs to be balanced with kMaxAgentMapObservationsCount
   sampling_seed: 42 # If given, the set of scenes to sample from will be deterministic, if None, the set of scenes will be random
   obs_radius: 50.0 # Visibility radius of the agents
-  action_space_steer_disc: 15
-  action_space_accel_disc: 11
-  max_steer_angle: -1.57  # pi/2 = 1.57, pi/3 = 1.05
+  action_space_steer_disc: 13
+  action_space_accel_disc: 7
+  max_steer_angle: 1.57  # pi/2 = 1.57, pi/3 = 1.05
   max_accel_value: 4.0
   init_steps: 0 # Warmup steps
   goal_achieved_weight: 0.0
@@ -51,14 +52,14 @@ environment: # Overrides default environment configs (see pygpudrive/env/config.
 wandb:
   entity: ""
   project: "humanlike"
-  group: "wosac_1"
+  group: "debug_mini"  
   mode: "online" # Options: online, offline, disabled
   tags: ["ppo", "ff"]
 
 train:
   exp_id: guidance_logs # Set dynamically in the script if needed
   seed: 42
-  cpu_offload: true
+  cpu_offload: false
   device: "cuda" # Dynamically set to cuda if available, else cpu
   bptt_horizon: 1
   compile: false
@@ -67,7 +68,7 @@ train:
   # # # Data sampling # # #
   resample_scenes: false
   resample_dataset_size: 10_000 # Number of unique scenes to sample from
-  resample_interval: 2_000_000
+  resample_interval: 5_000_000
   sample_with_replacement: false
   shuffle_dataset: true
   file_prefix: ""
@@ -75,7 +76,7 @@ train:
   # # # PPO # # #
   torch_deterministic: false
   total_timesteps: 4_000_000_000
-  batch_size: 131072
+  batch_size: 65536
   minibatch_size: 8192
   learning_rate: 3e-4
   anneal_lr: true
@@ -86,7 +87,7 @@ train:
   clip_coef: 0.2
   clip_vloss: false
   vf_clip_coef: 0.2
-  ent_coef: 0.003
+  ent_coef: 0.01
   vf_coef: 0.5
   max_grad_norm: 0.5
   target_kl: null
@@ -94,24 +95,24 @@ train:
   # # # Logging # # #
   log_window: 100
   track_realism_metrics: true # Log human-like metrics
-  track_n_worlds: 2 # Number of worlds to track
+  track_n_worlds: 3 # Number of worlds to track
 
   # # # Network # # #
   network:
-    embed_dim: 64 # Embedding of the input features
+    embed_dim: 256 # Embedding of the input features
     dropout: 0.01
     class_name: "Agent"
     num_parameters: 0 # Total trainable parameters, to be filled at runtime
 
   # # # Checkpointing # # #
-  checkpoint_interval: 500 # Save policy every k iterations
+  checkpoint_interval: 200 # Save policy every k iterations
   checkpoint_path: "./runs"
 
   # # # Rendering # # #
   render: true # Determines whether to render the environment (note: will slow down training)
   render_3d: false # Render simulator state in 3d or 2d
-  render_interval: 500 # Render every k iterations
-  render_every_t: 10 # Render every k timesteps
+  render_interval: 400 # Render every k iterations
+  render_every_t: 5 # Render every k timesteps
   render_k_scenarios: 1 # Number of scenarios to render
   render_agent_idx: [0] # Agent observations to render
   render_format: "mp4" # Options: gif, mp4
diff --git a/baselines/ppo/ppo_guided_autonomy.py b/baselines/ppo/ppo_guided_autonomy.py
index ff279ae7e..13383892e 100644
--- a/baselines/ppo/ppo_guided_autonomy.py
+++ b/baselines/ppo/ppo_guided_autonomy.py
@@ -45,6 +45,26 @@ def load_config(config_path):
 
 def make_agent(env, config):
     """Create a policy based on the environment."""
+    
+    if config.continue_training:
+        print("Loading checkpoint...")
+        # Load checkpoint
+        saved_cpt = torch.load(
+        f=config.model_cpt,
+            weights_only=False,
+        )
+
+        # Create policy architecture from saved checkpoint
+        agent = Agent(
+            config=saved_cpt["config"],
+            embed_dim=saved_cpt["model_arch"]["embed_dim"],
+            action_dim=saved_cpt["action_dim"],
+        )
+
+        # Load the model parameters
+        agent.load_state_dict(saved_cpt["parameters"])
+        return agent
+    
     return Agent(
         config=config.environment,
         embed_dim=config.train.network.embed_dim,
@@ -124,6 +144,7 @@ def run(
     add_reference_heading: Annotated[Optional[int], typer.Option(help="0 or 1")] = None,
     add_previous_action: Annotated[Optional[int], typer.Option(help="0 or 1")] = None,
     smoothen_trajectory: Annotated[Optional[int], typer.Option(help="0 or 1")] = None,
+    guidance_dropout_prob: Annotated[Optional[float], typer.Option(help="The dropout probability for the guidance")] = None,
 
     smoothness_weight: Annotated[Optional[float], typer.Option(help="Scale for realism rewards")] = None,
     dist_to_goal_threshold: Annotated[Optional[float], typer.Option(help="The distance threshold for goal-achieved")] = None,
@@ -189,6 +210,7 @@ def run(
         "sampling_seed": sampling_seed,
         "obs_radius": obs_radius,
         "collision_behavior": collision_behavior,
+        "guidance_dropout_prob": guidance_dropout_prob,
         "remove_non_vehicles": None
         if remove_non_vehicles is None
         else bool(remove_non_vehicles),
diff --git a/examples/eval/figures_data/reference_log_replay.npy b/examples/eval/figures_data/reference_log_replay.npy
deleted file mode 100644
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diff --git a/examples/eval/notebooks/01_wosac_results.ipynb b/examples/eval/notebooks/01_wosac_results.ipynb
index c63b2ad1c..0ae68570d 100644
--- a/examples/eval/notebooks/01_wosac_results.ipynb
+++ b/examples/eval/notebooks/01_wosac_results.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10,7 +10,45 @@
     "import os\n",
     "import glob\n",
     "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "import importlib\n",
+    "import gpudrive\n",
+    "importlib.reload(gpudrive)\n",
+    "\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import torch\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# Set working directory to the base directory 'gpudrive_madrona'\n",
+    "working_dir = Path.cwd()\n",
+    "while working_dir.name != 'gpudrive':\n",
+    "    working_dir = working_dir.parent\n",
+    "    if working_dir == Path.home():\n",
+    "        raise FileNotFoundError(\"Base directory 'gpudrive_madrona' not found\")\n",
+    "os.chdir(working_dir)\n",
+    "import torch\n",
+    "from PIL import Image\n",
+    "import seaborn as sns\n",
+    "import warnings\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import datetime\n",
+    "\n",
+    "from gpudrive.env.env_torch import GPUDriveTorchEnv\n",
+    "from gpudrive.env.config import EnvConfig, RenderConfig\n",
+    "from gpudrive.env.dataset import SceneDataLoader\n",
+    "from gpudrive.visualize.utils import img_from_fig\n",
+    "\n",
+    "sns.set(\"notebook\", font_scale=1.05, rc={\"figure.figsize\": (10, 5)})\n",
+    "sns.set_style(\"ticks\", rc={\"figure.facecolor\": \"none\", \"axes.facecolor\": \"none\"})\n",
+    "%config InlineBackend.figure_format = 'svg'\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "plt.set_loglevel(\"WARNING\")\n",
+    "mpl.rcParams[\"lines.markersize\"] = 8\n",
+    "\n",
+    "plt.set_loglevel(\"WARNING\")\n",
+    "%matplotlib inline"
    ]
   },
   {
@@ -376,17 +414,1758 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
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+       " <metadata>\n",
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+       "   <cc:Work>\n",
+       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
+       "    <dc:date>2025-05-12T19:05:00.649751</dc:date>\n",
+       "    <dc:format>image/svg+xml</dc:format>\n",
+       "    <dc:creator>\n",
+       "     <cc:Agent>\n",
+       "      <dc:title>Matplotlib v3.9.0, https://matplotlib.org/</dc:title>\n",
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+       "   </g>\n",
+       "  </g>\n",
+       " </g>\n",
+       " <defs>\n",
+       "  <clipPath id=\"pcedc36b84c\">\n",
+       "   <rect x=\"47.591813\" y=\"7.2\" width=\"196.941176\" height=\"166.32\"/>\n",
+       "  </clipPath>\n",
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+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 1200x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(12, 3))\n",
+    "\n",
+    "axs[0].hist(df_dist[\"metametric\"], bins=10)\n",
+    "axs[0].set_ylabel(\"Count\")\n",
+    "axs[0].set_xlabel(\"WOSAC composite metric\")\n",
+    "axs[0].grid(True, alpha=.3)\n",
+    "\n",
+    "axs[1].hist(df_dist[\"average_displacement_error\"], bins=10)\n",
+    "axs[1].set_ylabel(\"Count\")\n",
+    "axs[1].set_xlabel(\"Average displacement error\")\n",
+    "axs[1].grid(True, alpha=.3)\n",
+    "\n",
+    "axs[2].hist(df_dist[\"simulated_offroad_rate\"], bins=10)\n",
+    "axs[2].set_ylabel(\"Count\")\n",
+    "axs[2].set_xlabel(\"Offroad rate\")\n",
+    "axs[2].grid(True, alpha=.3)\n",
+    "\n",
+    "#sns.histplot(df_dist, x=\"metametric\", bins=50, kde=False)\n",
+    "\n",
+    "sns.despine()"
+   ]
   },
   {
    "cell_type": "code",
@@ -395,12 +2174,1185 @@
    "outputs": [
     {
      "data": {
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       "text/plain": [
-       "<Figure size 600x500 with 1 Axes>"
+       "<Figure size 1000x500 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -415,6 +3367,25 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/eval/notebooks/02_guidance_data_analysis.ipynb b/examples/eval/notebooks/02_guidance_data_analysis.ipynb
index 19af0e2ec..60eae77a4 100644
--- a/examples/eval/notebooks/02_guidance_data_analysis.ipynb
+++ b/examples/eval/notebooks/02_guidance_data_analysis.ipynb
@@ -2,7 +2,11 @@
  "cells": [
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 1,
+=======
    "execution_count": 52,
+>>>>>>> dev
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,6 +50,47 @@
     "%matplotlib inline"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MS = 9\n",
+    " \n",
+    "def plot_agent_guidance_info(guidance_obs, center_agent_idx):\n",
+    " \n",
+    "    agent_guidance = guidance_obs[center_agent_idx, :, :]\n",
+    "    mask = agent_guidance[:, 0] != -1.0\n",
+    "    \n",
+    "    time_steps = np.arange(agent_guidance.shape[0])\n",
+    "    \n",
+    "    fig, axes = plt.subplots(1, 3, figsize=(9.5, 3))\n",
+    "  \n",
+    "    axes[0].scatter(agent_guidance[:, 0][mask], agent_guidance[:, 1][mask], marker='o', s=MS, color='g')  \n",
+    "    axes[0].set_xlabel(r'Suggested $x$')\n",
+    "    axes[0].set_ylabel(r'Suggested $y$')\n",
+    "    #axes[0].legend()\n",
+    "    axes[0].grid(True, alpha=0.3)\n",
+    "    \n",
+    "    axes[1].scatter(time_steps[mask], agent_guidance[:, 2][mask], marker='o', color='b', s=MS)\n",
+    "    axes[1].set_xlabel('Time')\n",
+    "    axes[1].set_ylabel('Suggested speed')\n",
+    "    axes[1].grid(True, alpha=0.3)\n",
+    "    \n",
+    "    axes[2].scatter(time_steps[mask], agent_guidance[:, 3][mask], marker='o', color='#bc6c25', s=MS)\n",
+    "    axes[2].set_xlabel('Time')\n",
+    "    axes[2].set_ylabel('Suggested heading (rad)')\n",
+    "    axes[2].grid(True, alpha=0.3)\n",
+    "    \n",
+    "    #fig.suptitle(f\"Agent's {center_agent_idx} normalized guidance information\")\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.subplots_adjust(top=0.85, bottom=0.15)\n",
+    "    \n",
+    "    return fig"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -55,7 +100,11 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 3,
+=======
    "execution_count": 53,
+>>>>>>> dev
    "metadata": {},
    "outputs": [],
    "source": [
@@ -73,14 +122,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 4,
+=======
    "execution_count": 39,
+>>>>>>> dev
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['data/processed/wosac/validation_json_100/12858b7f79387840.json', 'data/processed/wosac/validation_json_100/14929c51c7298df1.json', 'data/processed/wosac/validation_json_100/14d7108a34259337.json', 'data/processed/wosac/validation_json_100/17c4e1e5a9665445.json']\n"
+      "['data/processed/wosac/validation_json_100/12858b7f79387840.json']\n"
      ]
     }
    ],
@@ -88,7 +141,6 @@
     "GUIDANCE_MODE = \"log_replay\"\n",
     "\n",
     "env_config = EnvConfig(\n",
-    "    dynamics_model=\"classic\",\n",
     "    reward_type=\"guided_autonomy\",\n",
     "    guidance=True,\n",
     "    guidance_mode=GUIDANCE_MODE,\n",
@@ -96,29 +148,45 @@
     "    add_reference_speed=True,\n",
     "    add_reference_pos_xy=True,\n",
     "    init_mode=\"wosac_train\",\n",
-    "    smoothen_trajectory=True,\n",
+    "    smoothen_trajectory=False,\n",
+    "    dynamics_model=\"delta_local\",\n",
+    "    guidance_dropout_prob=0.7,\n",
+    "    init_steps=0,\n",
     ")\n",
     "render_config = RenderConfig()\n",
     "\n",
     "train_loader = SceneDataLoader(\n",
     "    root=DATASET,\n",
-    "    batch_size=4,\n",
+    "    batch_size=1,\n",
     "    dataset_size=100,\n",
     "    sample_with_replacement=False,\n",
     "    shuffle=False,\n",
     "    file_prefix=\"\",\n",
+    "    \n",
     ")\n",
     "\n",
     "env = GPUDriveTorchEnv(\n",
     "    config=env_config,\n",
     "    data_loader=train_loader,\n",
-    "    max_cont_agents=64, \n",
+    "    max_cont_agents=32, \n",
     "    device=\"cpu\",\n",
     ")\n",
     "\n",
     "print(env.data_batch)\n",
     "\n",
     "obs = env.reset(env.cont_agent_mask)\n",
+    "expert_actions, _, _, _ = env.get_expert_actions()\n",
+    "\n",
+    "# for time_step in range(env.init_steps, 20):\n",
+    "#     print(f\"Step: {env.step_in_world[0, 0, 0].item()}\")\n",
+    "\n",
+    "#     # Step the environment\n",
+    "#     expert_actions, _, _, _ = env.get_expert_actions()\n",
+    "#     env.step_dynamics(expert_actions[:, :, time_step, :])\n",
+    "\n",
+    "#     obs = env.get_obs(env.cont_agent_mask)\n",
+    "#     rew = env.get_rewards()\n",
+    "    \n",
     "\n",
     "# # Save for analysis\n",
     "# reference_traj = torch.cat([\n",
@@ -134,6 +202,24 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([10, 2974])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "obs.shape"
+=======
    "execution_count": 41,
    "metadata": {},
    "outputs": [],
@@ -146,11 +232,6519 @@
    "metadata": {},
    "source": [
     "### Smoothening trajectory data"
+>>>>>>> dev
    ]
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 6,
+=======
    "execution_count": 42,
+>>>>>>> dev
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim_states = env.vis.plot_simulator_state(\n",
+    "    env_indices=0,\n",
+    "    zoom_radius=60,\n",
+    "    plot_guidance_pos_xy=True,\n",
+    "    plot_guidance_up_to_time=False,\n",
+    "    center_agent_indices=[0],\n",
+    ")\n",
+    "fig = sim_states[0]\n",
+    "\n",
+    "fig.savefig(\n",
+    "    os.path.join(FIGURES_DIR, f\"wosac_adapted_sim_state.pdf\"),\n",
+    "    dpi=300,\n",
+    "    bbox_inches=\"tight\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
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+   "source": [
+    "CENTER_AGENT_IDX = 0\n",
+    "\n",
+    "fig = env.vis.plot_agent_observation(\n",
+    "    env_idx=0,\n",
+    "    agent_idx=CENTER_AGENT_IDX,\n",
+    "    figsize=(10, 10),\n",
+    "    trajectory=env.reference_path[CENTER_AGENT_IDX, :, :],\n",
+    "    # step_reward=env.guidance_reward[\n",
+    "    #     0, 1\n",
+    "    # ].item(),\n",
+    "    # route_progress=env.route_progress[1],\n",
+    ")\n",
+    "\n",
+    "plt.savefig(\n",
+    "    os.path.join(FIGURES_DIR, f\"agent_observation_{CENTER_AGENT_IDX}_{GUIDANCE_MODE}.pdf\"),\n",
+    "    dpi=300,\n",
+    "    bbox_inches=\"tight\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
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+      "text/plain": [
+       "<Figure size 950x300 with 3 Axes>"
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+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plot_agent_guidance_info(env.guidance_obs, center_agent_idx=1);\n",
+    "\n",
+    "plt.savefig(\n",
+    "    os.path.join(FIGURES_DIR, f\"agent_guidance_info_{CENTER_AGENT_IDX}_{GUIDANCE_MODE}.pdf\"),\n",
+    "    dpi=300,\n",
+    "    bbox_inches=\"tight\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "         -1.7352,  -0.9873,  -0.2267,   0.5480,   1.3348,   2.1340,   2.9464])"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "env.reference_trajectory.pos_xy[0, 0, :, 0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Smoothening trajectory data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,7 +6754,10 @@
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   {
    "cell_type": "code",
+   "execution_count": 35,
+=======
    "execution_count": 43,
+>>>>>>> dev
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/examples/eval/notebooks/obtain_guidance_data.py b/examples/eval/notebooks/obtain_guidance_data.py
index 59ed9981d..f81b3c286 100644
--- a/examples/eval/notebooks/obtain_guidance_data.py
+++ b/examples/eval/notebooks/obtain_guidance_data.py
@@ -11,7 +11,7 @@
 
 if __name__ == "__main__":
 
-    GUIDANCE_MODE = "vbd_online"
+    GUIDANCE_MODE = "log_replay"
     DATASET = "data/processed/wosac/validation_json_100"  # Ensure VBD trajectory structures are in here
     SAVE_PATH = "examples/eval/figures_data/"
 
@@ -41,7 +41,7 @@
         config=env_config,
         data_loader=train_loader,
         max_cont_agents=64,
-        device="cpu",
+        device="cuda",
     )
 
     obs = env.reset(env.cont_agent_mask)
diff --git a/examples/eval/notebooks/variability_in_dataset.ipynb b/examples/eval/notebooks/variability_in_dataset.ipynb
new file mode 100644
index 000000000..7e301058f
--- /dev/null
+++ b/examples/eval/notebooks/variability_in_dataset.ipynb
@@ -0,0 +1,1716 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "0fa8c70e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import importlib\n",
+    "import gpudrive\n",
+    "importlib.reload(gpudrive)\n",
+    "\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import torch\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# Set working directory to the base directory 'gpudrive_madrona'\n",
+    "working_dir = Path.cwd()\n",
+    "while working_dir.name != 'gpudrive':\n",
+    "    working_dir = working_dir.parent\n",
+    "    if working_dir == Path.home():\n",
+    "        raise FileNotFoundError(\"Base directory 'gpudrive_madrona' not found\")\n",
+    "os.chdir(working_dir)\n",
+    "import torch\n",
+    "from PIL import Image\n",
+    "import seaborn as sns\n",
+    "import warnings\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import datetime\n",
+    "\n",
+    "from gpudrive.env.env_torch import GPUDriveTorchEnv\n",
+    "from gpudrive.env.config import EnvConfig, RenderConfig\n",
+    "from gpudrive.env.dataset import SceneDataLoader\n",
+    "from gpudrive.visualize.utils import img_from_fig\n",
+    "\n",
+    "sns.set(\"notebook\", font_scale=1.05, rc={\"figure.figsize\": (10, 5)})\n",
+    "sns.set_style(\"ticks\", rc={\"figure.facecolor\": \"none\", \"axes.facecolor\": \"none\"})\n",
+    "%config InlineBackend.figure_format = 'svg'\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "plt.set_loglevel(\"WARNING\")\n",
+    "mpl.rcParams[\"lines.markersize\"] = 8\n",
+    "\n",
+    "plt.set_loglevel(\"WARNING\")\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "7d2f76fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATASET = \"data/processed/wosac/validation_json_100\"\n",
+    "FIGURES_DIR = \"examples/eval/figures\"\n",
+    "DATA_FOLDER = \"examples/eval/figures_data/\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6933650b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5435, 91, 6)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ref_log_replay = np.load(f\"{DATA_FOLDER}reference_log_replay_smooth.npy\")\n",
+    "\n",
+    "ref_log_replay.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "7301cdce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "valid_guidance_points = ref_log_replay[:, :, 5].sum(axis=-1)\n",
+    "valid_guidance_points.shape\n",
+    "\n",
+    "# Some trajectories, while valid, are just parked cars\n",
+    "# Detect these by checking for zero velocity trajectories\n",
+    "valid_and_non_zero = ((ref_log_replay[:, :, 2] != 0) & (ref_log_replay[:, :, 5] == 1)).sum(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "1f401802",
+   "metadata": {},
+   "outputs": [
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+       " <defs>\n",
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+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 900x350 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 2, figsize=(9, 3.5), sharey=True)\n",
+    "\n",
+    "#fig.suptitle(f\"Distribution of guidance points per agent; A = {ref_log_replay.shape[0]}\")\n",
+    "\n",
+    "sns.histplot(valid_guidance_points, bins=10, stat='percent', ax=axs[0])\n",
+    "axs[0].grid(True, alpha=0.2)\n",
+    "axs[0].set_xlabel(\"Guidance points per agent\")\n",
+    "mean_value = valid_guidance_points.mean()\n",
+    "axs[0].axvline(x=mean_value, color='purple', linestyle='--')\n",
+    "axs[0].text(mean_value-3, axs[0].get_ylim()[1]*0.95, r'$\\mu$', color='purple', \n",
+    "            fontsize=14, ha='center', va='center')\n",
+    "\n",
+    "sns.histplot(valid_and_non_zero, bins=10, stat='percent', ax=axs[1])\n",
+    "axs[1].grid(True, alpha=0.2)\n",
+    "axs[1].set_xlabel(r\"Guidance points per agent (nonzero excluded)\")\n",
+    "mean_value_nonzero = valid_and_non_zero.mean()\n",
+    "axs[1].axvline(x=mean_value_nonzero, color='purple', linestyle='--')\n",
+    "axs[1].text(mean_value_nonzero-3, axs[1].get_ylim()[1]*0.95, r'$\\mu$', color='purple', fontsize=14, ha='center', va='center')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "sns.despine()\n",
+    "plt.savefig(f\"{FIGURES_DIR}/guidance_points_distribution.pdf\", dpi=300, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "cf5219a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5435, 91, 6)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ref_log_replay.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "f3cbb779",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5435, 91, 2)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Modalities\n",
+    "ref_log_replay[:, :, :2].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eecede8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(389141, 2)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ref_log_replay[:, :, :2].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5435, 91)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "is_valid = ref_log_replay[:, :, 5] == 1\n",
+    "\n",
+    "is_valid.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83686132",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "gpudrive",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/eval/rollout_test.py b/examples/eval/rollout_test.py
index 39c960423..ac40a3f97 100644
--- a/examples/eval/rollout_test.py
+++ b/examples/eval/rollout_test.py
@@ -1,37 +1,39 @@
 import numpy as np
 import mediapy as media
 import wandb
+import torch
 
 from gpudrive.env.env_torch import GPUDriveTorchEnv
 from gpudrive.env.config import EnvConfig
 from gpudrive.env.dataset import SceneDataLoader
 from gpudrive.visualize.utils import img_from_fig
+from gpudrive.utils.checkpoint import load_agent
 
-
-def test_rollout(focus_agents=[0, 1]):
+def test_rollout(focus_agents=[0, 1], render=False, agent=None):
     run = wandb.init(project="humanlike_tests", group="rollout_tests")
 
     env_config = EnvConfig(
         guidance=True,
-        guidance_mode="log_replay",  # Options: "log_replay", "vbd_amortized"
+        guidance_mode="vbd_amortized",  # Options: "log_replay", "vbd_amortized"
         add_reference_pos_xy=True,
         add_reference_speed=True,
         add_reference_heading=True,
         add_previous_action=True,
         reward_type="guided_autonomy",
         init_mode="wosac_train",
-        dynamics_model="delta_local",  # "state", #"classic",
+        dynamics_model="classic",  # "state", #"classic",
         smoothen_trajectory=True,
-        smoothness_weight=0.001,
-        collision_weight=-0.01,
-        off_road_weight=-0.01,
-        guidance_heading_weight=0.01,
-        guidance_speed_weight=0.01,
+        smoothness_weight=0.0,
+        collision_weight=-0.02,
+        off_road_weight=-0.0,
+        guidance_heading_weight=0.005,
+        guidance_speed_weight=0.0005,
+        
     )
 
     # Create data loader
     train_loader = SceneDataLoader(
-        root="data/processed/wosac/selected_json",
+        root="data/processed/wosac/validation_json_1",
         batch_size=1,
         dataset_size=100,
         sample_with_replacement=False,
@@ -43,47 +45,56 @@ def test_rollout(focus_agents=[0, 1]):
     env = GPUDriveTorchEnv(
         config=env_config,
         data_loader=train_loader,
-        max_cont_agents=64,  # Number of agents to control
+        max_cont_agents=32,  # Number of agents to control
         device="cuda",
     )
 
     control_mask = env.cont_agent_mask
 
     # Rollout
-    obs = env.reset(mask=control_mask)
+    next_obs = env.reset(mask=control_mask)
 
     sim_frames = []
     agent_obs_frames = {i: [] for i in focus_agents}
-    cum_reward = {i: 0 for i in focus_agents}
-
+    cum_reward = np.zeros((env.num_worlds, env.max_cont_agents))
+    
     expert_actions, _, _, _ = env.get_expert_actions()
 
     for time_step in range(env.init_steps, env.episode_len):
         print(f"Step: {env.step_in_world[0, 0, 0].item()}")
 
         # Step
-        expert_actions, _, _, _ = env.get_expert_actions()
-
-        # Sample random actions
-        # expert_actions = np.random.uniform(
-        #     low=-env_config.max_accel_value,
-
-        env.step_dynamics(expert_actions[:, :, time_step, :])
-
-        obs = env.get_obs(control_mask)
+        if agent is not None: # Rollout with pre-trained agent
+            control_mask = env.cont_agent_mask
+            action, _, _, _ = agent(next_obs)
+            action_template = torch.zeros(
+                (1, 32), dtype=torch.int64, device='cuda'
+            )
+            action_template[control_mask] = action.to('cuda')
+            
+            env.step_dynamics(action_template)
+            
+        else:
+            env.step_dynamics(expert_actions[:, :, time_step, :])
+
+        next_obs = env.get_obs(control_mask)
         reward = env.get_rewards()
         done = env.get_dones()
         info = env.get_infos()
 
-        for agent_idx in focus_agents:
-            cum_reward[agent_idx] += reward[0, agent_idx].item()
+        # for agent_idx in focus_agents:
+        #     cum_reward[agent_idx] += reward[0, agent_idx].item()
 
-        # Render
-        if time_step % 5 == 0 or time_step > env.episode_len - 3:
-            sim_states, agent_obs = env.render(focus_agent_idx=focus_agents)
-            sim_frames.append(img_from_fig(sim_states[0]))
-            for i in focus_agents:
-                agent_obs_frames[i].append(img_from_fig(agent_obs[i]))
+        cum_reward += reward.cpu().numpy()
+
+        if render:
+            if time_step % 5 == 0 or time_step > env.episode_len - 3:
+                sim_states, agent_obs = env.render(
+                    focus_agent_idx=focus_agents
+                )
+                sim_frames.append(img_from_fig(sim_states[0]))
+                for i in focus_agents:
+                    agent_obs_frames[i].append(img_from_fig(agent_obs[i]))
 
         # Log reward magnitudes
         for agent_idx in focus_agents:
@@ -103,18 +114,37 @@ def test_rollout(focus_agents=[0, 1]):
                     f"{agent_key}/R_route": env.route_reward[
                         0, agent_idx
                     ].item(),
-                    f"{agent_key}/R_cumulative": cum_reward[agent_idx],
+                    f"{agent_key}/R_cumulative": cum_reward[
+                        0, agent_idx
+                    ].item(),
                 },
                 step=env.step_in_world[0, 0, 0].item(),
             )
+            
+    avg_cum_reward = cum_reward[env.cont_agent_mask.cpu().numpy()].mean()
 
-    for agent_idx in focus_agents:
-        agent_key = f"agent_{agent_idx}"
-        agent_obs_arr = np.array(agent_obs_frames[agent_idx])
+    print(
+        f"Avg cumulative rewards N = {env.cont_agent_mask.sum()}: {avg_cum_reward}"
+    )
+    if render:
+        for agent_idx in focus_agents:
+            agent_key = f"agent_{agent_idx}"
+            agent_obs_arr = np.array(agent_obs_frames[agent_idx])
+            wandb.log(
+                {
+                    f"{agent_key}/render": wandb.Video(
+                        np.moveaxis(agent_obs_arr, -1, 1),
+                        fps=5,
+                        format="mp4",
+                    )
+                }
+            )
+            
+        sim_frames_arr = np.array(sim_frames)
         wandb.log(
             {
-                f"{agent_key}/render": wandb.Video(
-                    np.moveaxis(agent_obs_arr, -1, 1),
+                "sim/render": wandb.Video(
+                    np.moveaxis(sim_frames_arr, -1, 1),
                     fps=5,
                     format="mp4",
                 )
@@ -126,4 +156,14 @@ def test_rollout(focus_agents=[0, 1]):
 
 
 if __name__ == "__main__":
-    test_rollout(focus_agents=[0, 1, 2, 3, 4])
+    
+    
+    # load policy    
+    CPT_PATH = (
+        "checkpoints/model_guidance_logs__S_1__05_12_18_44_58_857_000100.pt"
+    )
+    agent = load_agent(path_to_cpt=CPT_PATH).to("cuda")
+
+    
+    
+    test_rollout(focus_agents=[0, 1, 2, 3, 4], render=True, agent=agent)
diff --git a/examples/eval/run_wosac_eval.py b/examples/eval/run_wosac_eval.py
index 7657a1941..ad589cabf 100644
--- a/examples/eval/run_wosac_eval.py
+++ b/examples/eval/run_wosac_eval.py
@@ -8,6 +8,8 @@
 from time import perf_counter
 from tqdm import tqdm
 from pathlib import Path
+from box import Box
+import yaml
 
 from gpudrive.env.config import EnvConfig
 from gpudrive.env.env_torch import GPUDriveTorchEnv
@@ -64,16 +66,12 @@ def rollout(
     device: str,
     render_simulator_states: bool = False,
     render_agent_pov: bool = False,
-    render_every_n_steps: int = 2,
+    render_every_n_steps: int = 5,
     save_videos: bool = True,
     video_dir: str = "videos",
     video_format: str = "gif",
 ):
     """Rollout agent in the environment and return the scenario rollouts."""
-
-    if save_videos:
-        os.makedirs(video_dir, exist_ok=True)
-
     # Storage
     env_ids = list(range(num_envs))
     simulator_state_frames = {env_id: [] for env_id in range(num_envs)}
@@ -94,8 +92,8 @@ def rollout(
         info.collided_with_vehicle == 1
     ) & (info.type == int(madrona_gpudrive.EntityType.Vehicle))
     control_mask = control_mask_all & ~zero_action_mask
-
-    next_obs = env.reset(control_mask)
+    
+    next_obs = env.reset(mask=control_mask)
 
     # Get scenario ids
     scenario_ids_dict = env.get_scenario_ids()
@@ -126,7 +124,7 @@ def rollout(
         if render_simulator_states and time_step % render_every_n_steps == 0:
             sim_states = env.vis.plot_simulator_state(
                 env_indices=env_ids,
-                zoom_radius=120,
+                zoom_radius=100,
                 time_steps=[time_step] * len(env_ids),
                 plot_guidance_pos_xy=True,
             )
@@ -145,7 +143,9 @@ def rollout(
             agent_observation_frames[idx].append(img_from_fig(agent_obs))
 
         # Get next observation
-        next_obs = env.get_obs(control_mask)
+        next_obs = env.get_obs(control_mask)          
+        # NOTE(dc): Make sure to decouple the obs from the reward function
+        reward = env.get_rewards()
         done = env.get_dones()
 
         pos_x, pos_y, pos_z, heading, id = get_state(env)
@@ -235,25 +235,33 @@ def rollout(
 
     return scenario_ids, scenario_rollouts, scenario_rollout_masks
 
+def load_config(config_path):
+    """Load the configuration file."""
+    with open(config_path, "r") as f:
+        config = Box(yaml.safe_load(f))
+    return config
+
 
 if __name__ == "__main__":
 
     # Settings
-    MAX_AGENTS = 64
+    MAX_AGENTS = 32  # TODO: Set to 128 for real eval
     NUM_ENVS = 20
     DEVICE = "cuda"  # where to run the env rollouts
     NUM_ROLLOUTS_PER_BATCH = 1
     NUM_DATA_BATCHES = 1
     INIT_STEPS = 10
     DATASET_SIZE = 100
-    RENDER = True
+    RENDER = False
+    LOG_DIR = "examples/eval/figures_data/wosac/"
+    GUIDANCE_MODE = "log_replay" #"vbd_amortized"
 
     DATA_JSON = "data/processed/wosac/validation_json_100"
     DATA_TFRECORD = "data/processed/wosac/validation_tfrecord_100"
-    # CPT_PATH = "checkpoints/model_guidance_progress__S_1__05_04_17_37_18_741_001677.pt" # .73 meta-score on single_scene (10 rollouts)
-    # https://wandb.ai/emerge_/humanlike/runs/guidance_progress__S_1__05_04_17_37_18_741?nw=nwuserdaphnecor
 
-    CPT_PATH = "checkpoints/model_guidance_progress__S_100__05_06_20_02_22_663_001500.pt"
+    CPT_PATH = (
+        "checkpoints/model_guidance_logs__S_100__05_12_23_14_58_678_001000.pt"
+    )
 
     # Create data loader
     val_loader = SceneDataLoader(
@@ -267,27 +275,59 @@ def rollout(
 
     # Load agent
     agent = load_agent(path_to_cpt=CPT_PATH).to(DEVICE)
+    
+    # config = load_config("baselines/ppo/config/ppo_guided_autonomy.yaml")
+    # config = config.environment
+    
+    config = agent.config
 
     # Override default environment settings to match those the agent was trained with
-    default_config = EnvConfig()
-    config_dict = {
-        field.name: getattr(agent.config, field.name)
-        for field in dataclasses.fields(EnvConfig)
-        if hasattr(agent.config, field.name)
-        and getattr(agent.config, field.name)
-        != getattr(default_config, field.name)
-    }
-
-    # Add fixed overrides specific to WOSAC evaluation
-    config_dict["init_steps"] = INIT_STEPS
-    config_dict["init_mode"] = "wosac_eval"
-
-    logging.info(
-        f"initializing env with init_mode = {config_dict['init_mode']}"
+    # TODO(dc): Clean this up
+    env_config = EnvConfig(
+        ego_state=config.ego_state,
+        road_map_obs=config.road_map_obs,
+        partner_obs=config.partner_obs,
+        reward_type=config.reward_type,
+        guidance_speed_weight=config.guidance_speed_weight,
+        guidance_heading_weight=config.guidance_heading_weight,
+        smoothness_weight=config.smoothness_weight,
+        norm_obs=config.norm_obs,
+        add_previous_action=config.add_previous_action,
+        guidance=config.guidance,
+        guidance_dropout_prob=config.guidance_dropout_prob,
+        add_reference_pos_xy=config.add_reference_pos_xy,
+        add_reference_speed=config.add_reference_speed,
+        add_reference_heading=config.add_reference_heading,
+        smoothen_trajectory=config.smoothen_trajectory,
+        dynamics_model=config.dynamics_model,
+        collision_behavior=config.collision_behavior,
+        goal_behavior=config.goal_behavior,
+        polyline_reduction_threshold=config.polyline_reduction_threshold,
+        remove_non_vehicles=config.remove_non_vehicles,
+        lidar_obs=False, 
+        obs_radius=config.obs_radius,
+        max_steer_angle=config.max_steer_angle,
+        max_accel_value=config.max_accel_value,
+        action_space_steer_disc=config.action_space_steer_disc,
+        action_space_accel_disc=config.action_space_accel_disc,
+        # Override action space
+        steer_actions = torch.round(
+            torch.linspace(
+                -config.max_steer_angle, config.max_steer_angle, config.action_space_steer_disc
+            ),
+            decimals=3,
+            ),
+        accel_actions = torch.round(
+                torch.linspace(
+                    -config.max_accel_value, config.max_accel_value, config.action_space_accel_disc
+                ),
+                decimals=3,
+            ),
+        init_mode="wosac_eval",
+        init_steps=INIT_STEPS,
+        guidance_mode=GUIDANCE_MODE,
     )
 
-    env_config = dataclasses.replace(default_config, **config_dict)
-
     # Make environment
     env = GPUDriveTorchEnv(
         config=env_config,
@@ -296,7 +336,10 @@ def rollout(
         device=DEVICE,
     )
 
-    wosac_metrics = WOSACMetrics()
+    wosac_metrics = WOSACMetrics(
+        save_table_with_baselines=True,
+        log_dir=LOG_DIR,
+    )
 
     for _ in tqdm(range(NUM_DATA_BATCHES)):
         for _ in range(NUM_ROLLOUTS_PER_BATCH):
diff --git a/examples/eval/wosac_eval_origin.py b/examples/eval/wosac_eval_origin.py
index c83ab0d97..fa4364b69 100644
--- a/examples/eval/wosac_eval_origin.py
+++ b/examples/eval/wosac_eval_origin.py
@@ -84,12 +84,14 @@ def __init__(
         if log_dir is not None:
             # Get data and time for the current run as filename
             log_dir = Path(log_dir)
-            self.log_file_path = log_dir / f"{self.timestamp}.csv"
+            self.log_file_path = log_dir / f"score_distribution_{self.timestamp}.csv"
+            self.log_aggregate_table_path = log_dir / f"wosac_table_{self.timestamp}.csv"
             log_dir.mkdir(parents=True, exist_ok=True)
             self.log_file = csv.writer(open(self.log_file_path, 'w'))
             # Add header to the log file
-            self.log_file.writerow(self.field_names) 
-
+            all_rows = self.field_names.copy()
+            all_rows.insert(0, "scenario_id")
+            self.log_file.writerow(all_rows) 
 
         else:
             self.log_file = None
@@ -241,8 +243,9 @@ def update(
                 if self.log_file is not None:
                     # Log the scenario metrics to the file  
                     result_to_log = [
+                        _scenario_rollout.scenario_id,
                         scenario_result.metametric,
-                        scenario_result.average_displacement_error
+                        scenario_result.average_displacement_error,
                         scenario_result.linear_speed_likelihood,
                         scenario_result.linear_acceleration_likelihood,
                         scenario_result.angular_speed_likelihood,
@@ -336,9 +339,8 @@ def compute(self) -> Dict[str, Tensor]:
         if self.save_table_with_baselines:
             self._add_current_method_to_baselines(metrics_dict, final_metrics)
 
-            table_name = f"wosac_table_{self.timestamp}.csv"
-            self.baselines_df.to_csv(table_name, index=False)
-            print(f"Saved df to {table_name}")
+            self.baselines_df.to_csv(self.log_aggregate_table_path, index=False)
+            print(f"Saved df to {self.log_aggregate_table_path}")
 
         return out_dict
 
diff --git a/examples/experimental/notebooks/debug.ipynb b/examples/experimental/notebooks/debug.ipynb
index 9e00a48e0..e9baa772d 100644
--- a/examples/experimental/notebooks/debug.ipynb
+++ b/examples/experimental/notebooks/debug.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,13 +89,6 @@
     "    return fig"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -105,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -124,7 +117,7 @@
     "    add_reference_heading=True,\n",
     "    add_reference_speed=True,\n",
     "    add_reference_pos_xy=True,\n",
-    "    init_mode=\"womd_tracks_to_predict\"\n",
+    "    init_mode=\"wosac_train\"\n",
     ")\n",
     "render_config = RenderConfig()\n",
     "\n",
@@ -149,16 +142,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Controllable agents: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
-      "        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
-      "        36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51])\n",
+      "Controllable agents: tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8, 10, 13, 14, 15, 17, 18, 19, 20, 21,\n",
+      "        22, 23, 24, 25, 26, 27, 29, 30, 32, 34, 35, 36, 37, 38, 39, 40, 42, 43,\n",
+      "        44, 47, 48, 49, 50])\n",
       "Controlling agent 12 in env 0.\n"
      ]
     }
@@ -182,7 +175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -202,6363 +195,6 @@
     "### Show agent guidance information \n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
-       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
-       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
-       "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
-       "<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"712.64875pt\" height=\"253.48pt\" viewBox=\"0 0 712.64875 253.48\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n",
-       " <metadata>\n",
-       "  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n",
-       "   <cc:Work>\n",
-       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
-       "    <dc:date>2025-04-28T13:02:40.397071</dc:date>\n",
-       "    <dc:format>image/svg+xml</dc:format>\n",
-       "    <dc:creator>\n",
-       "     <cc:Agent>\n",
-       "      <dc:title>Matplotlib v3.9.0, https://matplotlib.org/</dc:title>\n",
-       "     </cc:Agent>\n",
-       "    </dc:creator>\n",
-       "   </cc:Work>\n",
-       "  </rdf:RDF>\n",
-       " </metadata>\n",
-       " <defs>\n",
-       "  <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
-       " </defs>\n",
-       " <g id=\"figure_1\">\n",
-       "  <g id=\"patch_1\">\n",
-       "   <path d=\"M 0 253.48 \n",
-       "L 712.64875 253.48 \n",
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-       "z\n",
-       "\" style=\"fill: none\"/>\n",
-       "  </g>\n",
-       "  <g id=\"axes_1\">\n",
-       "   <g id=\"patch_2\">\n",
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-       "   </g>\n",
-       "   <g id=\"matplotlib.axis_1\">\n",
-       "    <g id=\"xtick_1\">\n",
-       "     <g id=\"line2d_1\">\n",
-       "      <path d=\"M 71.744811 186.48 \n",
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-       "     </g>\n",
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diff --git a/gpudrive/datatypes/trajectory.py b/gpudrive/datatypes/trajectory.py
index f36dc5306..15bd58a89 100644
--- a/gpudrive/datatypes/trajectory.py
+++ b/gpudrive/datatypes/trajectory.py
@@ -216,17 +216,23 @@ class VBDTrajectory:
 
     def __init__(self, vbd_traj_tensor: torch.Tensor):
         """Initializes the VBD trajectory with a tensor."""
-        self.pos_x = vbd_traj_tensor[:, :, :, 0].unsqueeze(-1)
-        self.pos_y = vbd_traj_tensor[:, :, :, 1].unsqueeze(-1)
-        self.pos_xy = vbd_traj_tensor[:, :, :, :2]
-        self.yaw = wrap_yaws(vbd_traj_tensor[:, :, :, 2].unsqueeze(-1))
-        self.vel_x = vbd_traj_tensor[:, :, :, 3].unsqueeze(-1)
-        self.vel_y = vbd_traj_tensor[:, :, :, 4].unsqueeze(-1)
-        self.vel_xy = vbd_traj_tensor[:, :, :, 3:5]
+        # Pad to make sure the trajectory is of length 91
+        padding = (0, 0, 1, 0)
+
+        vbd_traj_tensor_pad = torch.nn.functional.pad(vbd_traj_tensor, padding, mode='constant', value=-1.0)
+
+        self.pos_x = vbd_traj_tensor_pad[:, :, :, 0].unsqueeze(-1)
+        self.pos_y = vbd_traj_tensor_pad[:, :, :, 1].unsqueeze(-1)
+        self.pos_xy = vbd_traj_tensor_pad[:, :, :, :2]
+        self.yaw = wrap_yaws(vbd_traj_tensor_pad[:, :, :, 2].unsqueeze(-1))
+        self.vel_x = vbd_traj_tensor_pad[:, :, :, 3].unsqueeze(-1)
+        self.vel_y = vbd_traj_tensor_pad[:, :, :, 4].unsqueeze(-1)
+        self.vel_xy = vbd_traj_tensor_pad[:, :, :, 3:5]
         self.ref_speed = self.comp_reference_speed()
-        self.valids = vbd_traj_tensor[:, :, :, 5].unsqueeze(-1).to(
+        self.valids = vbd_traj_tensor_pad[:, :, :, 5].unsqueeze(-1).to(
             torch.int32
         )
+        
 
     @classmethod
     def from_tensor(
diff --git a/gpudrive/env/config.py b/gpudrive/env/config.py
index 960128c29..e549a9013 100755
--- a/gpudrive/env/config.py
+++ b/gpudrive/env/config.py
@@ -30,7 +30,7 @@ class EnvConfig:
     partner_obs: bool = True  # Include partner vehicle info in observations
     bev_obs: bool = False  # Include rasterized Bird's Eye View observations centered on ego vehicle
     norm_obs: bool = True  # Normalize observations
-    add_previous_action: bool = False  # Previous action time agent has taken
+    add_previous_action: bool = True  # Previous action time agent has taken
 
     # Guidance settings; these are used to direct the agent's behavior and
     # will be included in the observations if set to True
@@ -38,10 +38,11 @@ class EnvConfig:
     guidance_mode: str = "log_replay"  # Options: "log_replay", "vbd_amortized", "vbd_online", "goals_only"
     # Ways to guide the agent
     add_reference_pos_xy: bool = True  # (x, y) position time series
-    add_reference_speed: bool = False  # speed time series
-    add_reference_heading: bool = False  # heading time series
-    smoothen_trajectory: bool = False  # Filters out the trajectory
+    add_reference_speed: bool = True  # speed time series
+    add_reference_heading: bool = True  # heading time series
+    smoothen_trajectory: bool = True  # Filters out the trajectory
     guidance_pos_xy_radius: float = 1.0  # Tightness of the positions guidance
+    guidance_dropout_prob: float = 0.0  # Probability of dropping the points
 
     # Maximum number of controlled agents in the scene
     max_controlled_agents: int = madrona_gpudrive.kMaxAgentCount
@@ -117,23 +118,23 @@ class EnvConfig:
     goal_behavior: str = "ignore"  # Options: "stop", "ignore", "remove"
 
     # Reward settings
-    reward_type: str = "sparse_on_goal_achieved"
+    reward_type: str = "guided_autonomy"
     # Alternatively, "weighted_combination", "guided_autonomy", "reward_conditioned"
 
     # If reward_type is "guided_autonomy", the following parameters are used
     guidance_speed_weight: float = (
-        0.01  # Importance of matching suggested speeds
+        0.005  # Importance of matching suggested speeds
     )
     guidance_heading_weight: float = (
-        0.01  # Importance of matching suggested headings
+        0.005 # Importance of matching suggested headings
     )
     smoothness_weight: float = 0.0
 
     # If reward_type is "reward_conditioned", the following parameters are used
     # Weights for the reward components
-    collision_weight: float = 0.0
+    collision_weight: float = -0.1
     goal_achieved_weight: float = 1.0
-    off_road_weight: float = 0.0
+    off_road_weight: float = -0.1
 
     condition_mode: str = "random"  # Options: "random", "fixed", "preset"
     # If condition_mode is "fixed", set the agent weights here
diff --git a/gpudrive/env/constants.py b/gpudrive/env/constants.py
index 1f77b79a5..bf5937da4 100644
--- a/gpudrive/env/constants.py
+++ b/gpudrive/env/constants.py
@@ -23,7 +23,7 @@
 MAX_ROAD_SCALE = 100
 
 # Feature shape constants
-EGO_FEAT_DIM = 7  # Ego state base fields
+EGO_FEAT_DIM = 6  # Ego state base fields
 PARTNER_FEAT_DIM = 6
 ROAD_GRAPH_FEAT_DIM = 13
 
diff --git a/gpudrive/env/env_puffer.py b/gpudrive/env/env_puffer.py
index 8eac76e00..e083a9ac4 100644
--- a/gpudrive/env/env_puffer.py
+++ b/gpudrive/env/env_puffer.py
@@ -195,6 +195,7 @@ def __init__(
         goal_achieved_weight=1,
         dist_to_goal_threshold=2.0,
         polyline_reduction_threshold=0.1,
+        guidance_dropout_prob=0.0,
         remove_non_vehicles=False,
         obs_radius=50.0,
         track_realism_metrics=False,
@@ -241,6 +242,7 @@ def __init__(
         self.add_reference_pos_xy = add_reference_pos_xy
         self.add_reference_speed = add_reference_speed
         self.add_reference_heading = add_reference_heading
+        self.guidance_dropout_prob = guidance_dropout_prob
 
         self.render = render
         self.render_interval = render_interval
@@ -276,6 +278,7 @@ def __init__(
             bev_obs=bev_obs,
             add_previous_action=add_previous_action,
             guidance=guidance,
+            guidance_dropout_prob=guidance_dropout_prob,
             add_reference_pos_xy=add_reference_pos_xy,
             add_reference_speed=add_reference_speed,
             add_reference_heading=add_reference_heading,
@@ -294,8 +297,19 @@ def __init__(
             max_accel_value=max_accel_value,
             action_space_steer_disc=action_space_steer_disc,
             action_space_accel_disc=action_space_accel_disc,
+            # Override action space
+            steer_actions = torch.round(torch.linspace(
+                    -max_steer_angle, max_steer_angle, action_space_steer_disc
+                ),
+                decimals=3,
+                ),
+            accel_actions = torch.round(
+                torch.linspace(
+                    -max_accel_value, max_accel_value, action_space_accel_disc
+                ),
+                decimals=3,
+            )
         )
-
         render_config = RenderConfig(
             render_3d=render_3d,
         )
@@ -755,6 +769,10 @@ def log_data_coverage(self):
         # Update the cumulative set (coverage)
         self.cumulative_unique_files.update(new_idx)
 
+        guidance_density = (
+            self.env.valid_guidance_points / self.env.reference_traj_len
+        )
+
         if self.wandb_obj is not None:
             self.wandb_obj.log(
                 {
@@ -768,6 +786,10 @@ def log_data_coverage(self):
                         / len(set(self.file_to_index))
                     )
                     * 100,
+                    "data/guidance_density_mean": guidance_density.mean().item(),
+                    "data/guidance_density_dist": wandb.Histogram(
+                        guidance_density.cpu().numpy()
+                    ),
                 },
                 step=self.global_step,
             )
diff --git a/gpudrive/env/env_torch.py b/gpudrive/env/env_torch.py
index cdb0fe3cf..dd91d6a09 100755
--- a/gpudrive/env/env_torch.py
+++ b/gpudrive/env/env_torch.py
@@ -73,13 +73,13 @@ def __init__(
 
         # Get the initial data batch (set of traffic scenarios)
         self.data_batch = next(self.data_iterator)
-        
+
         assert self.num_worlds == len(
             self.data_batch
         ), f"Number of scenarios in data_batch ({len(self.data_batch)}) \
         should equal number of worlds ({self.num_worlds}). \
         \n Please check your data loader configuration."
-        
+
         # Initialize simulator
         self.sim = self._initialize_simulator(params, self.data_batch)
 
@@ -123,6 +123,7 @@ def __init__(
             dtype=np.float32,
         )
 
+        # Action space setup
         # Action space setup
         self._setup_action_space(action_type)
         self.single_action_space = self.action_space
@@ -160,12 +161,12 @@ def setup_guidance(self):
             self.vbd_model = self._load_vbd_model(
                 model_path=self.config.vbd_model_path
             )
-            
+
             self.init_steps = max(self.init_steps, 10)
             print(
                 f"\n[Note] Guidance mode '{self.guidance_mode}' requires at least {self.init_steps} initialization steps to provide sufficient scene context for the diffusion model. Automatically setting simulator time to t = {self.init_steps}. \n"
             )
-            
+
             # Construct scene context dict for the VBD model
             scene_context = self.construct_context(init_steps=self.init_steps)
 
@@ -183,27 +184,29 @@ def setup_guidance(self):
                 self.backend,
                 self.device,
             )
-            
+
             reference_trajectory = torch.zeros(
                 self.num_worlds,
                 self.max_agent_count,
                 madrona_gpudrive.kTrajectoryLength,
-                6
+                6,
+            )
+            reference_trajectory[
+                :, :, : self.init_steps + 1, :2
+            ] = log_trajectory.pos_xy[:, :, : self.init_steps + 1]
+            reference_trajectory[
+                :, :, : self.init_steps + 1, 2
+            ] = log_trajectory.yaw[:, :, : self.init_steps + 1, 0]
+            reference_trajectory[
+                :, :, : self.init_steps + 1, 3:5
+            ] = log_trajectory.vel_xy[:, :, : self.init_steps + 1]
+            reference_trajectory[
+                :, :, : self.init_steps + 1, 5
+            ] = log_trajectory.valids[:, :, : self.init_steps + 1, 0]
+
+            vbd_predictions = (
+                predicted["denoised_trajs"].to(self.device).detach()
             )
-            reference_trajectory[:, :, :self.init_steps + 1, :2] = log_trajectory.pos_xy[
-                :, :, :self.init_steps + 1
-            ]
-            reference_trajectory[:, :, :self.init_steps + 1, 2] = log_trajectory.yaw[
-                :, :, :self.init_steps + 1, 0
-            ]
-            reference_trajectory[:, :, :self.init_steps + 1, 3:5] = log_trajectory.vel_xy[
-                :, :, :self.init_steps + 1
-            ]
-            reference_trajectory[:, :, :self.init_steps + 1, 5] = log_trajectory.valids[
-                :, :, :self.init_steps + 1, 0
-            ]
-
-            vbd_predictions=predicted["denoised_trajs"].to(self.device).detach()
 
             # Get the world means
             world_means = (
@@ -213,24 +216,25 @@ def setup_guidance(self):
             # Add vbd predictions to the reference trajectory
             for i in range(self.num_worlds):
                 # Get controlled agent indices for this world
-                valid_mask = (
-                    scene_context["agents_id"][i] >= 0
-                )
+                valid_mask = scene_context["agents_id"][i] >= 0
                 valid_world_indices = scene_context["agents_id"][i][valid_mask]
 
                 reference_trajectory[
-                    i, valid_world_indices, self.init_steps + 1:, :2
-                ] = vbd_predictions[i, valid_world_indices, :, :2] - world_means[
+                    i, valid_world_indices, self.init_steps + 1 :, :2
+                ] = vbd_predictions[
+                    i, valid_world_indices, :, :2
+                ] - world_means[
                     i
-                ].view(1, 1, 2)
+                ].view(
+                    1, 1, 2
+                )
                 reference_trajectory[
-                    i, valid_world_indices, self.init_steps + 1:, 2:5
+                    i, valid_world_indices, self.init_steps + 1 :, 2:5
                 ] = vbd_predictions[i, valid_world_indices, :, 2:5]
                 reference_trajectory[
-                    i, valid_world_indices, self.init_steps + 1:, 5
+                    i, valid_world_indices, self.init_steps + 1 :, 5
                 ] = 1
 
-            
             # Wrap predictions into a VBDTrajectoryOnline object
             self.reference_trajectory = VBDTrajectoryOnline.from_tensor(
                 vbd_predictions=reference_trajectory,
@@ -557,13 +561,18 @@ def reset(
             # Zero out only the reset environments
             self.previous_action_value_tensor[reset_mask] = 0.0
             # Reset the guidance points hit tensor for the reset environments
-            # TODO: Fix for asynchronous resets
+            # TODO(dc): Fix for asynchronous resets
             self.guidance_points_hit, _ = self.guidance_points_within_reach()
 
         else:
             self.previous_action_value_tensor.zero_()
             self.guidance_points_hit, _ = self.guidance_points_within_reach()
 
+        # Dropout mask for guidance points
+        # Assumption: all worlds are reset at the same time
+        if self.config.guidance_dropout_prob > 0:
+            self.guidance_dropout_mask = self.create_guidance_dropout_mask()
+
         return self.get_obs(mask)
 
     def get_dones(self):
@@ -650,6 +659,10 @@ def get_rewards(
 
             step_in_world = self.step_in_world[:, 0, :].squeeze(-1)
 
+            actual_agent_speed = (
+                self.sim.self_observation_tensor().to_torch()[:, :, 0].clone()
+            )
+
             # 1. Get base rewards defined by user
             self.base_rewards = (
                 collision_weight * collided + off_road_weight * off_road
@@ -741,7 +754,7 @@ def get_rewards(
                 guidance_points_hit_count
                 / (self.reference_trajectory.valids.sum(axis=[2, 3]) + 1e-5),
                 min=0,
-                max=1,
+                max=0.1,
             )
 
             # We want agents that are parked to stay parked, and generally
@@ -765,55 +778,25 @@ def get_rewards(
 
             completed_route_mask = (route_progress >= 0.99).float()
 
-            # b). Add a bonus for being close to the reference end position
+            # b). Jerk penalty for completed routes
             if torch.any(completed_route_mask > 0):
-                # Extract the last valid reference position for each world, agent
-                # valid_mask is [1, 64, 91] and reference_trajectory.pos_xy is [1, 64, 91, 2]
-                # We need to find the last valid position for each world-agent pair
-                agent_states = GlobalEgoState.from_tensor(
-                    self.sim.absolute_self_observation_tensor(),
-                    self.backend,
-                    self.device,
-                )
-
-                # Get indices of last valid positions for each reference trajectory
-                last_valid_indices = torch.argmax(
-                    is_valid
-                    * torch.arange(is_valid.shape[2], device=is_valid.device),
-                    dim=2,
-                )
-
-                # Create indices for gathering
-                batch_indices = torch.zeros_like(
-                    last_valid_indices
-                )  # Shape: [1, 64]
-                agent_indices = torch.arange(
-                    is_valid.shape[1], device=is_valid.device
-                ).expand_as(
-                    last_valid_indices
-                )  # Shape: [1, 64]
-
-                # Gather the last valid reference positions
-                last_valid_positions = self.reference_trajectory.pos_xy[
-                    batch_indices, agent_indices, last_valid_indices
-                ]
-
-                distance = torch.norm(
-                    agent_states.pos_xy - last_valid_positions, dim=2
-                )
-
-                # Add bonus for being close to the reference end position
-                # Using exponential decay: bonus = scale * exp(-distance/th)
-                distance_threshold = 0.5
-                max_position_bonus = 0.01
-
-                position_bonus = max_position_bonus * torch.exp(
-                    -distance / distance_threshold
-                )
+                # Apply a penalty for jerk because we want agents to keep going straight
+                # when they have already passed the end of the route
+                # if they turn, the jerk penalty should cancel out the position bonus
+                if hasattr(self, "action_diff"):
+                    acceleration_jerk = self.action_diff[:, :, 0] ** 2
+                    steering_jerk = self.action_diff[:, :, 1] ** 2
+
+                    acceleration_penalty = 1.0 - torch.exp(-acceleration_jerk)
+                    steering_penalty = 1.0 - torch.exp(-steering_jerk)
+
+                    jerk_penalty = -0.001 * (
+                        acceleration_penalty + steering_penalty
+                    )
 
-                position_bonus = position_bonus * completed_route_mask
+                end_of_route_jerk = jerk_penalty * completed_route_mask
 
-                self.route_reward += position_bonus
+                self.route_reward += end_of_route_jerk
 
             self.guidance_reward = self.route_reward.clone()
 
@@ -837,10 +820,6 @@ def get_rewards(
                     .bool()
                 )
 
-                actual_agent_speed = (
-                    self.sim.self_observation_tensor().to_torch()[:, :, 0]
-                )
-
                 actual_agent_heading = self.agent_states.rotation_angle
 
                 # Compute distances
@@ -897,6 +876,56 @@ def get_rewards(
 
             return rewards
 
+    def create_guidance_dropout_mask(self):
+        """
+        Create guidance dropout mask where dropout_prob represents
+        the maximum dropout probability, with varying rates per trajectory.
+        """
+        max_dropout_prob = self.config.guidance_dropout_prob
+        num_controlled = self.cont_agent_mask.sum().item()
+
+        # 1 if we want to keep the point, 0 if we want to drop it
+        guidance_dropout_mask = torch.ones(
+            (num_controlled, self.reference_traj_len),
+            device=self.device,
+            dtype=torch.bool,
+        )
+
+        is_valid = (
+            self.reference_trajectory.valids[self.cont_agent_mask]
+            .squeeze(-1)
+            .bool()
+        )
+
+        # Generate random dropout rates for each agent between 0 and max_dropout_prob
+        agent_dropout_probs = (
+            torch.rand(num_controlled, device=self.device) * max_dropout_prob
+        )
+
+        for agent_idx in range(num_controlled):
+            agent_valid_mask = is_valid[agent_idx]
+            agent_valid_indices = torch.where(agent_valid_mask)[0]
+
+            # Get agent-specific dropout probability
+            agent_dropout_prob = agent_dropout_probs[agent_idx]
+
+            if (
+                len(agent_valid_indices) > 2
+            ):  # Only apply dropout if we have more than 2 points
+                # Keep first and last points, apply dropout to middle points
+                middle_indices = agent_valid_indices[1:-1]
+
+                # Generate random dropout mask for middle points
+                dropout = (
+                    torch.rand(len(middle_indices), device=self.device)
+                    < agent_dropout_prob
+                )
+
+                # Apply dropout to middle points (set to False for points to drop)
+                guidance_dropout_mask[agent_idx, middle_indices] = ~dropout
+
+        return guidance_dropout_mask
+
     def guidance_points_within_reach(self):
         # Get actual agent positions
         self.agent_states = GlobalEgoState.from_tensor(
@@ -913,7 +942,9 @@ def guidance_points_within_reach(self):
             dim=-1,
         )
 
-        points_within_reach = distances < self.config.dist_to_goal_threshold
+        points_within_reach = (
+            distances < self.config.dist_to_goal_threshold
+        ) & self.reference_trajectory.valids.bool().squeeze(-1)
 
         return points_within_reach, distances
 
@@ -1095,6 +1126,7 @@ def _get_guidance(self, mask=None) -> torch.Tensor:
             return torch.zeros(0, device=self.device)
 
         guidance = []
+        guidance_orig = []
 
         if mask is None:
 
@@ -1180,8 +1212,6 @@ def _get_guidance(self, mask=None) -> torch.Tensor:
             return torch.cat(guidance, dim=-1).flatten(start_dim=2)
 
         else:
-            batch_size = mask.sum()
-
             valid_timesteps_mask = self.reference_trajectory.valids.bool()[
                 mask
             ]
@@ -1198,12 +1228,16 @@ def _get_guidance(self, mask=None) -> torch.Tensor:
                 )
 
             if self.config.add_reference_speed:
-                reference_speed = (
-                    self.reference_trajectory.ref_speed[mask].clone()
-                    / constants.MAX_SPEED
-                )
+                reference_speed = self.reference_trajectory.ref_speed[
+                    mask
+                ].clone()
                 reference_speed[~valid_timesteps_mask] = constants.INVALID_ID
-                guidance.append(reference_speed)
+
+                reference_speed_normalized = (
+                    reference_speed / constants.MAX_SPEED
+                )
+                guidance_orig.append(reference_speed)
+                guidance.append(reference_speed_normalized)
 
             if self.config.add_reference_pos_xy:
                 global_reference_xy = self.reference_trajectory.pos_xy.clone()[
@@ -1246,6 +1280,7 @@ def _get_guidance(self, mask=None) -> torch.Tensor:
                 self.reference_path = local_reference_xy_orig
 
                 guidance.append(reference_path)
+                guidance_orig.append(local_reference_xy_orig)
 
             if self.config.add_reference_heading:
                 reference_headings = self.reference_trajectory.yaw[
@@ -1259,7 +1294,7 @@ def _get_guidance(self, mask=None) -> torch.Tensor:
                 )
 
                 # Normalize by 2pi to ensure values are in [-1, 1]
-                reference_headings = (
+                reference_headings_normalized = (
                     reference_headings / constants.MAX_ORIENTATION_RAD
                 )
 
@@ -1267,11 +1302,30 @@ def _get_guidance(self, mask=None) -> torch.Tensor:
                 reference_headings[
                     ~valid_timesteps_mask
                 ] = constants.INVALID_ID
-                guidance.append(reference_headings)
+                guidance.append(reference_headings_normalized)
+                guidance_orig.append(reference_headings)
 
-        self.guidance_obs = torch.cat(guidance, dim=-1)
+        self.guidance_obs = torch.cat(guidance_orig, dim=-1)
+
+        # Apply dropout mask if specified
+        # Note: currently only supported for masked observations
+        if self.config.guidance_dropout_prob > 0 and hasattr(
+            self, "guidance_dropout_mask"
+        ):
+            self.guidance_obs[
+                ~self.guidance_dropout_mask
+            ] = constants.INVALID_ID
+            self.reference_path[
+                ~self.guidance_dropout_mask
+            ] = constants.INVALID_ID
+
+        self.valid_guidance_points = torch.sum(
+            self.guidance_obs[:, :, 0] != constants.INVALID_ID, axis=1
+        )
 
-        return self.guidance_obs.flatten(start_dim=1)
+        guidance = torch.cat(guidance, dim=-1)
+
+        return guidance.flatten(start_dim=1)
 
     def _get_ego_state(self, mask=None) -> torch.Tensor:
         """Get the ego state."""
@@ -1293,7 +1347,6 @@ def _get_ego_state(self, mask=None) -> torch.Tensor:
             ego_state.speed.unsqueeze(-1),
             ego_state.vehicle_length.unsqueeze(-1),
             ego_state.vehicle_width.unsqueeze(-1),
-            ego_state.is_collided.unsqueeze(-1),
             ego_state.steer_angle.unsqueeze(-1),
         ]
 
@@ -1352,7 +1405,7 @@ def _get_ego_state(self, mask=None) -> torch.Tensor:
             # Handle the case where there are zero valid points
             # If total_valid is 0, we consider the route 100% complete (progress = 1.0)
             self.route_progress = torch.where(
-                total_valid > 0,
+                total_valid >= 1,
                 valid_hits_so_far / (total_valid + 1e-5),  # Normal case
                 torch.ones_like(
                     valid_hits_so_far
@@ -1754,10 +1807,36 @@ def render(self, focus_env_idx=0, focus_agent_idx=[0, 1]):
 
         return sim_states, agent_views
 
+    def render(self, focus_env_idx=0, focus_agent_idx=[0, 1]):
+        """Quick rendering function for debugging."""
+
+        sim_states = self.vis.plot_simulator_state(
+            env_indices=[focus_env_idx],
+            zoom_radius=70,
+            time_steps=[self.step_in_world[0, 0, 0].item()],
+            plot_guidance_pos_xy=True,
+        )
+
+        agent_views = []
+        for agent_idx in focus_agent_idx:
+            agent_obs = self.vis.plot_agent_observation(
+                env_idx=focus_env_idx,
+                agent_idx=agent_idx,
+                figsize=(10, 10),
+                trajectory=self.reference_path[agent_idx, :, :],
+                step_reward=self.guidance_reward[
+                    focus_env_idx, agent_idx
+                ].item(),
+                route_progress=self.route_progress[agent_idx],
+            )
+            agent_views.append(agent_obs)
+
+        return sim_states, agent_views
+
 
 if __name__ == "__main__":
 
-    FOCUS_AGENTS = [0, 1, 2, 3]
+    FOCUS_AGENTS = [0, 1, 2, 3, 4]
 
     env_config = EnvConfig(
         guidance=True,
@@ -1770,12 +1849,13 @@ def render(self, focus_env_idx=0, focus_agent_idx=[0, 1]):
         dynamics_model="delta_local",  # "state", #"classic",
         smoothen_trajectory=False,
         add_previous_action=True,
+        guidance_dropout_prob=0.9,  # 0.95,
     )
     render_config = RenderConfig()
 
     # Create data loader
     train_loader = SceneDataLoader(
-        root="data/processed/wosac/validation_json_1",
+        root="data/processed/wosac/validation_json_100",
         batch_size=1,
         dataset_size=100,
         sample_with_replacement=False,
@@ -1787,7 +1867,7 @@ def render(self, focus_env_idx=0, focus_agent_idx=[0, 1]):
     env = GPUDriveTorchEnv(
         config=env_config,
         data_loader=train_loader,
-        max_cont_agents=64,  # Number of agents to control
+        max_cont_agents=32,  # Number of agents to control
         device="cpu",
     )
 
diff --git a/gpudrive/integrations/puffer/ppo.py b/gpudrive/integrations/puffer/ppo.py
index 034990e3f..beb8936aa 100644
--- a/gpudrive/integrations/puffer/ppo.py
+++ b/gpudrive/integrations/puffer/ppo.py
@@ -754,11 +754,9 @@ def save_checkpoint(data, save_checkpoint_to_wandb=True):
 
     return model_path
 
-
 def count_params(policy):
     return sum(p.numel() for p in policy.parameters() if p.requires_grad)
 
-
 def seed_everything(seed, torch_deterministic):
     random.seed(seed)
     np.random.seed(seed)
diff --git a/gpudrive/networks/agents.py b/gpudrive/networks/agents.py
index 8468d30c2..65af8191c 100644
--- a/gpudrive/networks/agents.py
+++ b/gpudrive/networks/agents.py
@@ -52,7 +52,6 @@ def __init__(
         self.partner_obs_idx = self.ego_state_idx + (
             constants.PARTNER_FEAT_DIM * self.max_observable_agents
         )
-
         self.road_map_idx = self.partner_obs_idx + (
             constants.ROAD_GRAPH_TOP_K * constants.ROAD_GRAPH_FEAT_DIM
         )
@@ -65,7 +64,7 @@ def __init__(
                     constants.LOG_TRAJECTORY_LENGTH * 2
                 )
             if self.config["add_reference_speed"]:
-                # TODO: Change to reference path-length (90) if using the vbd_amortized trajs
+                # Assumption: Guidance traj is of length 91
                 self.guidance_feature_dim += constants.LOG_TRAJECTORY_LENGTH
             if self.config["add_reference_heading"]:
                 self.guidance_feature_dim += constants.LOG_TRAJECTORY_LENGTH
@@ -73,11 +72,11 @@ def __init__(
 
         # Shared embedding networks for both actor and critic
         self.ego_embed = nn.Sequential(
-            layer_init(nn.Linear(self.ego_state_idx, embed_dim)),
-            nn.LayerNorm(embed_dim),
+            layer_init(nn.Linear(self.ego_state_idx, 64)),
+            nn.LayerNorm(64),
             self.act_func,
             nn.Dropout(self.dropout),
-            layer_init(nn.Linear(embed_dim, embed_dim)),
+            layer_init(nn.Linear(64, 64)),
         )
 
         self.partner_embed = nn.Sequential(
@@ -106,7 +105,10 @@ def __init__(
 
         # Critic network
         self.critic = nn.Sequential(
-            layer_init(nn.Linear((2 * top_k + 2) * embed_dim, 64)),
+            layer_init(nn.Linear(((2 * top_k + 1) * embed_dim) + 64, 256)),
+            nn.LayerNorm(256),
+            self.act_func,
+            layer_init(nn.Linear(256, 64)),
             nn.LayerNorm(64),
             self.act_func,
             layer_init(nn.Linear(64, 1), std=1.0),
@@ -114,7 +116,10 @@ def __init__(
 
         # Actor network
         self.actor = nn.Sequential(
-            layer_init(nn.Linear((2 * top_k + 2) * embed_dim, 64)),
+            layer_init(nn.Linear(((2 * top_k + 1) * embed_dim) + 64, 256)),
+            nn.LayerNorm(256),
+            self.act_func,
+            layer_init(nn.Linear(256, 64)),
             nn.LayerNorm(64),
             self.act_func,
             layer_init(nn.Linear(64, action_dim), std=0.01),
@@ -282,7 +287,7 @@ def __init__(
             self.act_func,
             layer_init(
                 nn.Linear(32, 1), std=1.0
-            ),  # Fixed the dimension (was 32)
+            ),  
         )
 
         # Actor network
diff --git a/gpudrive/utils/generate_sbatch.py b/gpudrive/utils/generate_sbatch.py
index b5982886a..07db7ea8e 100644
--- a/gpudrive/utils/generate_sbatch.py
+++ b/gpudrive/utils/generate_sbatch.py
@@ -5,7 +5,7 @@
 import numpy as np
 import os
 import re
-from typing import Dict
+from typing import Dict, List, Optional
 from dotenv import load_dotenv
 
 # Load environment variables from .env file
@@ -21,6 +21,8 @@
 # Set to python file to run
 RUN_FILE = "baselines/ppo/ppo_pufferlib.py"
 
+SUPPORTED_GPU_TYPES = ["rtx8000", "v100", "a100"]
+
 # Default SLURM fields
 DEFAULT_SLURM_FIELDS = {
     "num_nodes": 1,
@@ -41,6 +43,7 @@
     "singularity_image": SINGULARITY_IMAGE,
     "overlay_file": OVERLAY_FILE,
     "run_file": RUN_FILE,
+    "exclude_nodes": [],  # GPU types to exclude
 }
 
 # a template for the submit script
@@ -58,8 +61,9 @@
 #SBATCH --time={time_d}-{time_h}:{time_m}:{time_s}
 #SBATCH --nodes={num_nodes}
 #SBATCH --cpus-per-task={num_cpus}
-#SBATCH --gres=gpu:{num_gpus}
+#SBATCH --gres=gpu:{num_gpus}{gpu_type_str}
 #SBATCH --account={account}
+{exclude_str}
 
 SINGULARITY_IMAGE={singularity_image}
 OVERLAY_FILE={overlay_file}
@@ -167,22 +171,23 @@ def _get_params_bash(params, values):
 
 
 def get_script(
-    fields: Dict = DEFAULT_SLURM_FIELDS, params: Dict = {}, param_order=None
+    fields: Dict = DEFAULT_SLURM_FIELDS, params: Dict = {}, param_order=None, exclude_nodes: List[str] = None
 ):
     """
     returns a string of a SLURM submission script using the passed fields
     and which creates an array of jobs which sweep the given params
 
-    fields:      dict of SLURM field names to their values. type is ignored
-    params:      a dict of (param names, param value list) pairs.
-                 The param name is the name of the bash variable created in
-                 the submission script which will contain the param's current
-                 value (for that SLURM job instance). param value list is
-                 a list (or range instance) of the values the param should take,
-                 to be run once against every other possible configuration of all params.
-    param_order: a list containing all param names which indicates the ordering
-                 of the params in the sweep. The last param changes every
-                 job number. If not supplied, uses an arbitrary order
+    fields:        dict of SLURM field names to their values. type is ignored
+    params:        a dict of (param names, param value list) pairs.
+                   The param name is the name of the bash variable created in
+                   the submission script which will contain the param's current
+                   value (for that SLURM job instance). param value list is
+                   a list (or range instance) of the values the param should take,
+                   to be run once against every other possible configuration of all params.
+    param_order:   a list containing all param names which indicates the ordering
+                   of the params in the sweep. The last param changes every
+                   job number. If not supplied, uses an arbitrary order
+    exclude_nodes: a list of GPU types to exclude (options: "rtx8000", "v100", "a100")
     """
 
     assert isinstance(fields, dict)
@@ -197,7 +202,7 @@ def get_script(
 
     # Check each field appears in the template
     for field in fields:
-        if ("{%s}" % field) not in TEMPLATE_SBATCH:
+        if ("{%s}" % field) not in TEMPLATE_SBATCH and field != "exclude_nodes":
             raise ValueError("passed field %s unused in template" % field)
 
     # Calculate total number of jobs (minus 1; SLURM is inclusive)
@@ -225,14 +230,31 @@ def get_script(
         subs[key] = val
     if "job_name" not in subs:
         subs["job_name"] = "my_job"
+    
+    # Handle gpu_type if specified
+    gpu_type_str = ""
+    if subs.get("gpu_type") is not None:
+        gpu_type = subs["gpu_type"]
+        if gpu_type.lower() in SUPPORTED_GPU_TYPES:
+            gpu_type_str = f":{gpu_type}"
+        else:
+            raise ValueError(f"Unsupported GPU type: {gpu_type}. Supported types are: {', '.join(SUPPORTED_GPU_TYPES)}")
+    subs["gpu_type_str"] = gpu_type_str
+    
+    # Handle exclude_nodes parameter
+    exclude_str = ""
+    if exclude_nodes and len(exclude_nodes) > 0:
+        exclude_nodes_cmd = ",".join(exclusion_commands)
+        exclude_str = f"#SBATCH --exclude={exclude_nodes_cmd}"
+    subs["exclude_str"] = exclude_str
 
     return TEMPLATE_SBATCH.format(**subs)
 
 
-def save_script(filename, file_path, fields, params, param_order=None):
+def save_script(filename, file_path, fields, params, param_order=None, exclude_nodes=None):
     """Generate and save sbatch (.sh) submission script."""
 
-    sbatch_script = get_script(fields, params, param_order)
+    sbatch_script = get_script(fields, params, param_order, exclude_nodes)
 
     if not file_path:
         os.makedirs(os.path.dirname(file_path), exist_ok=True)
@@ -243,38 +265,41 @@ def save_script(filename, file_path, fields, params, param_order=None):
 
 if __name__ == "__main__":
 
-    group = "wosac_scale"
-
+    group = "mem_optimization_2" 
     fields = {
-        "time_h": 47,  # Max time per job (job will finish if run is done before)
+        "time_h": 1,  # Max time per job (job will finish if run is done before)
         "num_gpus": 1,  # GPUs per job
         "max_sim_jobs": 30,  # Max jobs at the same time
-        "memory": 70,
+        "memory": 80,
         "job_name": group,
         "run_file": "baselines/ppo/ppo_guided_autonomy.py",
     }
 
     hyperparams = {
         "group": [group],  # Group name
-        "num_worlds": [700],
-        "resample_scenes": [0],
+        "num_worlds": [500, 700],
+        "data_dir": ["/scratch/kj2676/gpudrive/data/processed/training"],
+        "resample_scenes": [0, 1],
         "k_unique_scenes": [500],
-        # "resample_interval": [5_000_000],
+        "resample_interval": [5_000_000],
+        "cpu_offload": [0, 1],
+        "batch_size": [1],
         # "resample_dataset_size": [10_000],
         # "total_timesteps": [3_000_000_000],
-        "batch_size": [262_144],
-        "minibatch_size": [16_384],
-        # "guidance_speed_weight": [0.01],
-        # "guidance_heading_weight": [0.01],
-        "ent_coef": [0.001, 0.005, 0.003, 0.01],
-        "vf_coef": [0.5],
-        "update_epochs": [2, 4],
+        "batch_size": [1048576, 2097152],
+        # "minibatch_size": [16384],
+        # "ent_coef": [0.01, 0.003],
+        # "vf_coef": [0.5],
+        "update_epochs": [1],
         "render": [0],
     }
 
+    # NOTE: The exclusion part doesn't work atm, have to spefify the nodes manually
+    #exclude_nodes = ["v100", "rtx8000"] 
+
     save_script(
         file_path="examples/experimental/sbatch_scripts/",
         filename=f"sbatch_{group}.sh",
         fields=fields,
         params=hyperparams,
-    )
+    )
\ No newline at end of file
diff --git a/gpudrive/visualize/color.py b/gpudrive/visualize/color.py
index 75db464d5..f0cf2110c 100644
--- a/gpudrive/visualize/color.py
+++ b/gpudrive/visualize/color.py
@@ -25,15 +25,15 @@
 }
 
 AGENT_COLOR_BY_STATE = {
-    "ok": "#4B77BE",  # Controlled and doing fine
+    "ok": "#2a52be",  # Controlled and doing fine
     "collided": "r",  # Controlled and collided
     "off_road": "orange",  # Controlled and off-road
     "log_replay": "#787878",  # Agents marked as expert controlled or static
 }
 
 REL_OBS_OBJ_COLORS = {
-    "ego": "#0066ff",
-    "ego_goal": "#0099cc",
+    "ego": "#2a52be",
+    "ego_goal": "#2a52be",
     "other_agents": "#ff884d",
 }
 AGENT_COLOR_BY_POLICY = ["g", "b", "p"]
diff --git a/gpudrive/visualize/core.py b/gpudrive/visualize/core.py
index baa5c0ebf..b52dc8914 100644
--- a/gpudrive/visualize/core.py
+++ b/gpudrive/visualize/core.py
@@ -26,6 +26,7 @@
 )
 from gpudrive.datatypes.trajectory import LogTrajectory, VBDTrajectory
 from gpudrive.datatypes.control import ResponseType
+from gpudrive.env import constants
 from gpudrive.visualize.color import (
     ROAD_GRAPH_COLORS,
     ROAD_GRAPH_TYPE_NAMES,
@@ -100,6 +101,7 @@ def plot_simulator_state(
         center_agent_indices: Optional[List[int]] = None,
         zoom_radius: int = 100,
         plot_guidance_pos_xy: bool = False,
+        plot_guidance_up_to_time: bool = False,
         agent_positions: Optional[torch.Tensor] = None,
         backward_goals: bool = False,
         policy_masks: Optional[Dict[int, Dict[str, torch.Tensor]]] = None,
@@ -314,6 +316,7 @@ def plot_simulator_state(
                     env_idx=env_idx,
                     trajectory=self.trajectory,
                     line_width_scale=line_width_scale,
+                    plot_guidance_up_to_time=plot_guidance_up_to_time,
                 )
 
             # Draw the agents
@@ -380,6 +383,25 @@ def plot_simulator_state(
                 va="top",
             )
 
+            time_step = (
+                self.env_config.episode_len
+                - self.sim_object.steps_remaining_tensor().to_torch()[
+                    env_idx, 0
+                ]
+            ).item()
+
+            # Add time step text to the figure
+            ax.text(
+                0.05,  # x position in axes coordinates (5% from left)
+                0.95,  # y position in axes coordinates (95% from bottom)
+                f"t = {time_step}",
+                transform=ax.transAxes,  # Use axes coordinates
+                fontsize=15,
+                color="black",
+                ha="left",
+                va="top",
+            )
+
             # Set zoom window around the center
             ax.set_xlim(center_x - zoom_radius, center_x + zoom_radius)
             ax.set_ylim(center_y - zoom_radius, center_y + zoom_radius)
@@ -645,6 +667,7 @@ def _plot_reference_xy(
         control_mask: torch.Tensor,
         trajectory,
         line_width_scale: int = 1.0,
+        plot_guidance_up_to_time: bool = False,
     ):
         """Plot the log replay trajectory for controlled agents in either 2D or 3D."""
         if self.render_3d:
@@ -721,40 +744,122 @@ def _plot_reference_xy(
                 print(f"Error plotting 3D reference trajectory: {e}")
         else:
             try:
-                # Create a new scatter plot for this specific environment and control mask
-                pos_x = (
-                    trajectory.pos_xy.clone()[env_idx, control_mask, :, 0]
-                    .cpu()
-                    .numpy()
-                )
-                pos_y = (
-                    trajectory.pos_xy.clone()[env_idx, control_mask, :, 1]
-                    .cpu()
-                    .numpy()
-                )
 
-                # Filter out invalid points (zeros or out of bounds)
-                valid_mask = (
-                    (pos_x != 0)
-                    & (pos_y != 0)
-                    & (np.abs(pos_x) < OUT_OF_BOUNDS)
-                    & (np.abs(pos_y) < OUT_OF_BOUNDS)
-                )
+                if plot_guidance_up_to_time:
+                    # Get the time step for the current environment
+                    time_step = (
+                        self.env_config.episode_len
+                        - self.sim_object.steps_remaining_tensor().to_torch()[
+                            env_idx, 0
+                        ]
+                    ).item()
+
+                    # Limit the trajectory to the specified time step
+                    # Create a new scatter plot for this specific environment and control mask
+                    pos_x_context = (
+                        trajectory.pos_xy.clone()[
+                            env_idx, control_mask, :time_step, 0
+                        ]
+                        .cpu()
+                        .numpy()
+                    )
+                    pos_y_context = (
+                        trajectory.pos_xy.clone()[
+                            env_idx, control_mask, :time_step, 1
+                        ]
+                        .cpu()
+                        .numpy()
+                    )
 
-                # Apply mask if any valid points exist
-                if np.any(valid_mask):
-                    pos_x = pos_x[valid_mask]
-                    pos_y = pos_y[valid_mask]
+                    # Filter out invalid points (zeros or out of bounds)
+                    valid_mask = (
+                        (pos_x_context != 0)
+                        & (pos_y_context != 0)
+                        & (np.abs(pos_x_context) < OUT_OF_BOUNDS)
+                        & (np.abs(pos_y_context) < OUT_OF_BOUNDS)
+                    )
+
+                    ax.scatter(
+                        pos_x_context,
+                        pos_y_context,
+                        color="#f4a261",
+                        linewidth=0.1 * line_width_scale,
+                        alpha=0.7,
+                        s=25,
+                        zorder=0,
+                    )
+
+                    pos_x = (
+                        trajectory.pos_xy.clone()[
+                            env_idx, control_mask, time_step:, 0
+                        ]
+                        .cpu()
+                        .numpy()
+                    )
+                    pos_y = (
+                        trajectory.pos_xy.clone()[
+                            env_idx, control_mask, time_step:, 1
+                        ]
+                        .cpu()
+                        .numpy()
+                    )
+
+                    # Filter out invalid points (zeros or out of bounds)
+                    valid_mask = (
+                        (pos_x != 0)
+                        & (pos_y != 0)
+                        & (np.abs(pos_x) < OUT_OF_BOUNDS)
+                        & (np.abs(pos_y) < OUT_OF_BOUNDS)
+                    )
+
+                    # Apply mask if any valid points exist
+                    if np.any(valid_mask):
+                        pos_x = pos_x[valid_mask]
+                        pos_y = pos_y[valid_mask]
 
-                    # Create a fresh scatter plot
                     ax.scatter(
                         pos_x,
                         pos_y,
-                        color="lightgreen",
-                        linewidth=0.3 * line_width_scale,
+                        color="g",
+                        s=25,
                         alpha=0.25,
                         zorder=0,
                     )
+
+                else:
+                    pos_x = (
+                        trajectory.pos_xy.clone()[env_idx, control_mask, :, 0]
+                        .cpu()
+                        .numpy()
+                    )
+                    pos_y = (
+                        trajectory.pos_xy.clone()[env_idx, control_mask, :, 1]
+                        .cpu()
+                        .numpy()
+                    )
+
+                    # Filter out invalid points (zeros or out of bounds)
+                    valid_mask = (
+                        (pos_x != 0)
+                        & (pos_y != 0)
+                        & (np.abs(pos_x) < OUT_OF_BOUNDS)
+                        & (np.abs(pos_y) < OUT_OF_BOUNDS)
+                    )
+
+                    # Apply mask if any valid points exist
+                    if np.any(valid_mask):
+                        pos_x = pos_x[valid_mask]
+                        pos_y = pos_y[valid_mask]
+
+                    # Create a fresh scatter plot
+                    ax.scatter(
+                        pos_x,
+                        pos_y,
+                        color="g",
+                        s=25,
+                        alpha=0.1,
+                        zorder=0,
+                    )
             except Exception as e:
                 print(f"Error plotting 2D reference trajectory: {e}")
 
@@ -1130,7 +1235,7 @@ def _plot_roadgraph(
                             radius,
                             height,
                             facecolor="#c04000",
-                            alpha=0.9,
+                            alpha=0.7,
                         )
                 else:
                     for x, y in zip(x_coords, y_coords):
@@ -1141,7 +1246,7 @@ def _plot_roadgraph(
                             facecolor="#c04000",
                             edgecolor="none",
                             linewidth=3.0,
-                            alpha=0.9,
+                            alpha=0.6,
                         )
 
             elif road_point_type == int(madrona_gpudrive.EntityType.CrossWalk):
@@ -1259,7 +1364,7 @@ def _plot_filtered_agent_bounding_boxes(
         alpha: Optional[float] = 1.0,
         as_center_pts: bool = False,
         label: Optional[str] = None,
-        plot_goal_points: bool = True,
+        plot_goal_points: bool = False,
         line_width_scale: int = 1.0,
         marker_size_scale: int = 1.0,
         extended_goals: Optional[Dict[str, torch.Tensor]] = None,
@@ -1415,7 +1520,7 @@ def plot_agent_group_2d(bboxes, color, by_policy=False):
         # Plot goals
         if plot_goal_points:
             for mask, color in [
-                (is_ok_mask, AGENT_COLOR_BY_STATE["ok"]),
+                (is_ok_mask, "#f4a261"),  # AGENT_COLOR_BY_STATE["ok"]),
                 (is_offroad_mask, AGENT_COLOR_BY_STATE["off_road"]),
                 (is_collided_mask, AGENT_COLOR_BY_STATE["collided"]),
             ]:
@@ -1455,9 +1560,10 @@ def plot_agent_group_2d(bboxes, color, by_policy=False):
                         goal_x,
                         goal_y,
                         s=5 * marker_size_scale,
-                        linewidth=1.5 * line_width_scale,
+                        linewidth=2.0 * line_width_scale,
                         c=color,
                         marker="o",
+                        zorder=3,
                     )
                     for x, y in zip(goal_x, goal_y):
                         circle = Circle(
@@ -1466,6 +1572,8 @@ def plot_agent_group_2d(bboxes, color, by_policy=False):
                             color=color,
                             fill=False,
                             linestyle="--",
+                            linewidth=3 * line_width_scale,
+                            zorder=3,
                         )
                         ax.add_patch(circle)
 
@@ -1643,7 +1751,8 @@ def plot_agent_observation(
         line_width_scale = max(figsize) / 15
 
         fig, ax = plt.subplots(figsize=figsize)
-        ax.clear()  # Clear any previous plots
+        self._cleanup_axis(ax)
+
         ax.set_aspect("equal", adjustable="box")
 
         # Plot roadgraph if provided
@@ -1751,7 +1860,7 @@ def plot_agent_observation(
                 ].squeeze(),
                 color=REL_OBS_OBJ_COLORS["other_agents"],
                 alpha=1.0,
-                line_width_scale=line_width_scale,
+                line_width_scale=line_width_scale * 2.0,
             )
 
         if observation_ego is not None:
@@ -1776,7 +1885,7 @@ def plot_agent_observation(
                 orientation=0.0,
                 color=ego_agent_color,
                 alpha=1.0,
-                label="Ego agent",
+                line_width_scale=2.3,
             )
 
             # Add an arrow for speed
@@ -1790,7 +1899,7 @@ def plot_agent_observation(
                 head_length=1.1,
                 fc="k",
                 ec="k",
-                zorder=1,
+                zorder=10,
             )
 
         if lidar_obs is not None:
@@ -1827,16 +1936,16 @@ def plot_agent_observation(
             ]
         ).item()
 
-        # Add time step text to the figure
         ax.text(
-            0.05,  # x position in axes coordinates (5% from left)
-            0.90,  # y position in axes coordinates (95% from bottom)
-             r"$O_{t}$ for " + f"t = {time_step}",
-            transform=ax.transAxes,  # Use axes coordinates
+            0.05,
+            0.90,
+            r"$O_{t}$ for " + f"t = {time_step}",
+            transform=ax.transAxes,
             fontsize=15,
             color="black",
             ha="left",
             va="top",
+            bbox=dict(facecolor="white", alpha=1.0, edgecolor="none", pad=3),
         )
 
         if step_reward is not None:
@@ -1845,58 +1954,105 @@ def plot_agent_observation(
             )
 
             ax.text(
-                0.05,  # x position in axes coordinates (5% from left)
-                0.85,  # y position in axes coordinates (90% from bottom)
-                r"$R_{t+1} = $" + f"{step_reward:.2f}",
-                transform=ax.transAxes,  # Use axes coordinates
+                0.05,
+                0.85,
+                r"$R_{t+1} = $" + f"{step_reward:.3f}",
+                transform=ax.transAxes,
                 fontsize=15,
-                color=reward_color,  # Using the dynamically determined color
+                color=reward_color,
                 ha="left",
                 va="top",
+                bbox=dict(
+                    facecolor="white", alpha=1.0, edgecolor="none", pad=3
+                ),
             )
 
         if route_progress is not None:
-            # Add route process text to the figure
             ax.text(
-                0.05,  # x position in axes coordinates (5% from left)
-                0.80,  # y position in axes coordinates (85% from bottom)
+                0.05,
+                0.80,
                 f"Route progress = {route_progress:.2f}",
-                transform=ax.transAxes,  # Use axes coordinates
+                transform=ax.transAxes,
                 fontsize=15,
                 color="black",
                 ha="left",
                 va="top",
+                bbox=dict(
+                    facecolor="white", alpha=1.0, edgecolor="none", pad=3
+                ),
             )
 
         if trajectory is not None and len(trajectory) > 0:
+            mask = trajectory[:, 0] != constants.INVALID_ID
             # Plot the trajectory as a line
             ax.scatter(
-                trajectory[:, 0].cpu(),  # x coordinates
-                trajectory[:, 1].cpu(),  # y coordinates
+                trajectory[:, 0][mask].cpu(),  # x coordinates
+                trajectory[:, 1][mask].cpu(),  # y coordinates
                 color="g",
-                linewidth=0.05 * line_width_scale,
+                linewidth=0.01 * line_width_scale,
                 marker="o",
-                alpha=0.3,
+                alpha=0.6,
                 zorder=0,
             )
 
             # Draw a circle around every point in the trajectory
             for i in range(trajectory.shape[0]):
-                circle = Circle(
-                    (trajectory[i, 0].cpu(), trajectory[i, 1].cpu()),
-                    radius=self.env_config.guidance_pos_xy_radius,
-                    color="lightgrey",
-                    fill=False,
-                    linestyle="--",
-                    alpha=0.25,
-                )
-                ax.add_patch(circle)
-
+                if mask[i]:
+                    # Draw a circle around the trajectory point
+                    circle = Circle(
+                        (trajectory[i, 0].cpu(), trajectory[i, 1].cpu()),
+                        radius=self.env_config.guidance_pos_xy_radius,
+                        color="#d4a373",
+                        fill=False,
+                        linestyle="--",
+                        alpha=0.3,
+                    )
+                    ax.add_patch(circle)
 
         ax.set_xlim((-self.env_config.obs_radius, self.env_config.obs_radius))
         ax.set_ylim((-self.env_config.obs_radius, self.env_config.obs_radius))
+
+        # Add a circle representing the observation radius
+        observation_circle = Circle(
+            (0, 0),  # Center at origin
+            radius=self.env_config.obs_radius,
+            color="black",
+            fill=False,
+            linestyle="-",
+            linewidth=1.0,
+            alpha=0.7,
+        )
+
+        ax.add_patch(observation_circle)
         ax.set_xticks([])
         ax.set_yticks([])
         plt.axis("off")
 
         return fig
+
+    def _cleanup_axis(self, ax):
+        """Clean up all collections and artists from the axis."""
+        if self.render_3d:
+            # Clean 3D collections
+            for collection in ax.collections[:]:
+                collection.remove()
+
+            # Clean lines
+            for line in ax.lines[:]:
+                line.remove()
+        else:
+            # Clean 2D collections
+            for collection in ax.collections[:]:
+                collection.remove()
+
+            # Clean patches
+            for patch in ax.patches[:]:
+                patch.remove()
+
+            # Clean lines
+            for line in ax.lines[:]:
+                line.remove()
+
+            # Clean texts
+            for text in ax.texts[:]:
+                text.remove()
diff --git a/gpudrive/visualize/utils.py b/gpudrive/visualize/utils.py
index 5fa4a8566..93839e09f 100644
--- a/gpudrive/visualize/utils.py
+++ b/gpudrive/visualize/utils.py
@@ -134,7 +134,7 @@ def plot_numpy_bounding_boxes(
             [tl[1, :], tr[1, :], br[1, :], bl[1, :], tl[1, :]],
             color=color,
             zorder=4,
-            linewidth=1.7 * line_width_scale,
+            linewidth=2.0 * line_width_scale,
             alpha=alpha,
             label=label,
         )
diff --git a/src/consts.hpp b/src/consts.hpp
index 19200f798..604c9838c 100644
--- a/src/consts.hpp
+++ b/src/consts.hpp
@@ -10,7 +10,7 @@ namespace consts {
 
 inline constexpr madrona::CountT kMaxAgentCount = 32;
 inline constexpr madrona::CountT kMaxRoadEntityCount = 10000;
-inline constexpr madrona::CountT kMaxAgentMapObservationsCount = 128;
+inline constexpr madrona::CountT kMaxAgentMapObservationsCount = 186;
 
 inline constexpr bool useEstimatedYaw = true;
 
diff --git a/src/mgr.cpp b/src/mgr.cpp
index 092cb184a..ae6cbf2c9 100755
--- a/src/mgr.cpp
+++ b/src/mgr.cpp
@@ -908,7 +908,7 @@ Tensor Manager::vbdTrajectoryTensor() const {
         impl_->numWorlds,
         consts::kMaxAgentCount,
         consts::episodeLen,
-        5,
+        6,
     });
 }
 

From f1dd3be910091d5cf952c00d65a8ef6c9c36c256 Mon Sep 17 00:00:00 2001
From: Kevin Joseph <kevinwinston184@gmail.com>
Date: Tue, 13 May 2025 14:04:27 -0400
Subject: [PATCH 14/38] make vbd 91 steps (#446)

---
 gpudrive/datatypes/trajectory.py | 21 ++++++++-------------
 src/init.hpp                     |  2 +-
 src/level_gen.cpp                |  4 ++--
 src/mgr.cpp                      |  2 +-
 src/types.hpp                    |  6 +++---
 5 files changed, 15 insertions(+), 20 deletions(-)

diff --git a/gpudrive/datatypes/trajectory.py b/gpudrive/datatypes/trajectory.py
index 15bd58a89..30881eed5 100644
--- a/gpudrive/datatypes/trajectory.py
+++ b/gpudrive/datatypes/trajectory.py
@@ -216,20 +216,15 @@ class VBDTrajectory:
 
     def __init__(self, vbd_traj_tensor: torch.Tensor):
         """Initializes the VBD trajectory with a tensor."""
-        # Pad to make sure the trajectory is of length 91
-        padding = (0, 0, 1, 0)
-
-        vbd_traj_tensor_pad = torch.nn.functional.pad(vbd_traj_tensor, padding, mode='constant', value=-1.0)
-
-        self.pos_x = vbd_traj_tensor_pad[:, :, :, 0].unsqueeze(-1)
-        self.pos_y = vbd_traj_tensor_pad[:, :, :, 1].unsqueeze(-1)
-        self.pos_xy = vbd_traj_tensor_pad[:, :, :, :2]
-        self.yaw = wrap_yaws(vbd_traj_tensor_pad[:, :, :, 2].unsqueeze(-1))
-        self.vel_x = vbd_traj_tensor_pad[:, :, :, 3].unsqueeze(-1)
-        self.vel_y = vbd_traj_tensor_pad[:, :, :, 4].unsqueeze(-1)
-        self.vel_xy = vbd_traj_tensor_pad[:, :, :, 3:5]
+        self.pos_x = vbd_traj_tensor[:, :, :, 0].unsqueeze(-1)
+        self.pos_y = vbd_traj_tensor[:, :, :, 1].unsqueeze(-1)
+        self.pos_xy = vbd_traj_tensor[:, :, :, :2]
+        self.yaw = wrap_yaws(vbd_traj_tensor[:, :, :, 2].unsqueeze(-1))
+        self.vel_x = vbd_traj_tensor[:, :, :, 3].unsqueeze(-1)
+        self.vel_y = vbd_traj_tensor[:, :, :, 4].unsqueeze(-1)
+        self.vel_xy = vbd_traj_tensor[:, :, :, 3:5]
         self.ref_speed = self.comp_reference_speed()
-        self.valids = vbd_traj_tensor_pad[:, :, :, 5].unsqueeze(-1).to(
+        self.valids = vbd_traj_tensor[:, :, :, 5].unsqueeze(-1).to(
             torch.int32
         )
         
diff --git a/src/init.hpp b/src/init.hpp
index 8d4ff60dc..15b19dc11 100755
--- a/src/init.hpp
+++ b/src/init.hpp
@@ -37,7 +37,7 @@ namespace madrona_gpudrive
         uint32_t id;
         MapVector2 mean;
         bool markAsExpert{false};
-        float vbd_trajectories[consts::episodeLen][5];  // x, y, yaw, vx, vy
+        float vbd_trajectories[consts::kTrajectoryLength][6];  // x, y, yaw, vx, vy, valid
     };
 
     struct MapRoad
diff --git a/src/level_gen.cpp b/src/level_gen.cpp
index c43ad403f..06da6cb52 100755
--- a/src/level_gen.cpp
+++ b/src/level_gen.cpp
@@ -109,8 +109,8 @@ static inline void populateVBDTrajectory(Engine &ctx, const Entity &agent, const
     auto &vbd_trajectory = ctx.get<VBDTrajectory>(agent_iface);
     
     // Copy VBD trajectories from the map object
-    for (int i = 0; i < consts::episodeLen; i++) {
-        for (int j = 0; j < 5; j++) {
+    for (int i = 0; i < consts::kTrajectoryLength; i++) {
+        for (int j = 0; j < 6; j++) {
             vbd_trajectory.trajectories[i][j] = agentInit.vbd_trajectories[i][j];
         }
     }
diff --git a/src/mgr.cpp b/src/mgr.cpp
index ae6cbf2c9..55227b95a 100755
--- a/src/mgr.cpp
+++ b/src/mgr.cpp
@@ -907,7 +907,7 @@ Tensor Manager::vbdTrajectoryTensor() const {
     {
         impl_->numWorlds,
         consts::kMaxAgentCount,
-        consts::episodeLen,
+        consts::kTrajectoryLength,
         6,
     });
 }
diff --git a/src/types.hpp b/src/types.hpp
index b0fe159ab..c4823d66d 100755
--- a/src/types.hpp
+++ b/src/types.hpp
@@ -368,11 +368,11 @@ namespace madrona_gpudrive
     {
         // For each agent, store the full VBD trajectory (x, y, yaw, vx, vy, valid)
         // The tensor has shape [traj_length, 6]
-        float trajectories[consts::episodeLen][6];
+        float trajectories[consts::kTrajectoryLength][6];
 
         static inline void zero(VBDTrajectory& vbd_traj)
         {
-            for (int i = 0; i < consts::episodeLen; i++)
+            for (int i = 0; i < consts::kTrajectoryLength; i++)
             {
                 for (int j = 0; j < 6; j++)
                 {
@@ -382,7 +382,7 @@ namespace madrona_gpudrive
         }
     };
 
-    const size_t VBDTrajectoryExportSize = consts::episodeLen * 6;
+    const size_t VBDTrajectoryExportSize = consts::kTrajectoryLength * 6;
 
     static_assert(sizeof(VBDTrajectory) == sizeof(float) * VBDTrajectoryExportSize);
 

From 605cd4ba17e3053db0a18303cef3198483431ba5 Mon Sep 17 00:00:00 2001
From: Daphne Cornelisse <33460159+daphne-cornelisse@users.noreply.github.com>
Date: Tue, 13 May 2025 14:20:24 -0400
Subject: [PATCH 15/38] Fix dynamics model and some setting updates (#447)

* Fix: ensure vbd_online can be used on cuda and cpu.

* Small updates

* WOSAC defaults

* Dynamics model fix

* Minor
---
 baselines/ppo/config/ppo_guided_autonomy.yaml |     8 +-
 .../notebooks/02_guidance_data_analyis.ipynb  | 14960 ++++
 .../notebooks/02_guidance_data_analysis.ipynb | 63838 ----------------
 .../eval/notebooks/obtain_guidance_data.py    |    10 +-
 examples/eval/run_wosac_eval.py               |    39 +-
 examples/eval/wosac_eval_origin.py            |     2 +-
 gpudrive/datatypes/trajectory.py              |     1 -
 gpudrive/env/env_torch.py                     |     2 +-
 src/dynamics.hpp                              |     6 +-
 9 files changed, 14997 insertions(+), 63869 deletions(-)
 create mode 100644 examples/eval/notebooks/02_guidance_data_analyis.ipynb
 delete mode 100644 examples/eval/notebooks/02_guidance_data_analysis.ipynb

diff --git a/baselines/ppo/config/ppo_guided_autonomy.yaml b/baselines/ppo/config/ppo_guided_autonomy.yaml
index 985302f7c..a9302f829 100644
--- a/baselines/ppo/config/ppo_guided_autonomy.yaml
+++ b/baselines/ppo/config/ppo_guided_autonomy.yaml
@@ -28,8 +28,8 @@ environment: # Overrides default environment configs (see pygpudrive/env/config.
 
   # Reward function
   reward_type: "guided_autonomy"
-  collision_weight: -0.01
-  off_road_weight: -0.01 
+  collision_weight: -0.1
+  off_road_weight: -0.1
   guidance_speed_weight: 0.005
   guidance_heading_weight: 0.005
   smoothness_weight: 0.0
@@ -52,7 +52,7 @@ environment: # Overrides default environment configs (see pygpudrive/env/config.
 wandb:
   entity: ""
   project: "humanlike"
-  group: "debug_mini"  
+  group: "debug_mini"
   mode: "online" # Options: online, offline, disabled
   tags: ["ppo", "ff"]
 
@@ -69,7 +69,7 @@ train:
   resample_scenes: false
   resample_dataset_size: 10_000 # Number of unique scenes to sample from
   resample_interval: 5_000_000
-  sample_with_replacement: false
+  sample_with_replacement: true
   shuffle_dataset: true
   file_prefix: ""
 
diff --git a/examples/eval/notebooks/02_guidance_data_analyis.ipynb b/examples/eval/notebooks/02_guidance_data_analyis.ipynb
new file mode 100644
index 000000000..6018db22e
--- /dev/null
+++ b/examples/eval/notebooks/02_guidance_data_analyis.ipynb
@@ -0,0 +1,14960 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "ae958675",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import importlib\n",
+    "import gpudrive\n",
+    "importlib.reload(gpudrive)\n",
+    "\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import torch\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# Set working directory to the base directory 'gpudrive_madrona'\n",
+    "working_dir = Path.cwd()\n",
+    "while working_dir.name != 'gpudrive':\n",
+    "    working_dir = working_dir.parent\n",
+    "    if working_dir == Path.home():\n",
+    "        raise FileNotFoundError(\"Base directory 'gpudrive_madrona' not found\")\n",
+    "os.chdir(working_dir)\n",
+    "import torch\n",
+    "from PIL import Image\n",
+    "import seaborn as sns\n",
+    "import warnings\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import datetime\n",
+    "\n",
+    "from gpudrive.env.env_torch import GPUDriveTorchEnv\n",
+    "from gpudrive.env.config import EnvConfig, RenderConfig\n",
+    "from gpudrive.env.dataset import SceneDataLoader\n",
+    "from gpudrive.visualize.utils import img_from_fig\n",
+    "\n",
+    "sns.set(\"notebook\", font_scale=1.05, rc={\"figure.figsize\": (10, 5)})\n",
+    "sns.set_style(\"ticks\", rc={\"figure.facecolor\": \"none\", \"axes.facecolor\": \"none\"})\n",
+    "%config InlineBackend.figure_format = 'svg'\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "plt.set_loglevel(\"WARNING\")\n",
+    "mpl.rcParams[\"lines.markersize\"] = 8\n",
+    "\n",
+    "plt.set_loglevel(\"WARNING\")\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "937797aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MS = 9\n",
+    " \n",
+    "def plot_agent_guidance_info(guidance_obs, center_agent_idx):\n",
+    " \n",
+    "    agent_guidance = guidance_obs[center_agent_idx, :, :]\n",
+    "    mask = agent_guidance[:, 0] != -1.0\n",
+    "    \n",
+    "    time_steps = np.arange(agent_guidance.shape[0])\n",
+    "    \n",
+    "    fig, axes = plt.subplots(1, 3, figsize=(9.5, 3))\n",
+    "  \n",
+    "    axes[0].scatter(agent_guidance[:, 0][mask], agent_guidance[:, 1][mask], marker='o', s=MS, color='g')  \n",
+    "    axes[0].set_xlabel(r'Suggested $x$')\n",
+    "    axes[0].set_ylabel(r'Suggested $y$')\n",
+    "    #axes[0].legend()\n",
+    "    axes[0].grid(True, alpha=0.3)\n",
+    "    \n",
+    "    axes[1].scatter(time_steps[mask], agent_guidance[:, 2][mask], marker='o', color='b', s=MS)\n",
+    "    axes[1].set_xlabel('Time')\n",
+    "    axes[1].set_ylabel('Suggested speed')\n",
+    "    axes[1].grid(True, alpha=0.3)\n",
+    "    \n",
+    "    axes[2].scatter(time_steps[mask], agent_guidance[:, 3][mask], marker='o', color='#bc6c25', s=MS)\n",
+    "    axes[2].set_xlabel('Time')\n",
+    "    axes[2].set_ylabel('Suggested heading (rad)')\n",
+    "    axes[2].grid(True, alpha=0.3)\n",
+    "    \n",
+    "    #fig.suptitle(f\"Agent's {center_agent_idx} normalized guidance information\")\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.subplots_adjust(top=0.85, bottom=0.15)\n",
+    "    \n",
+    "    return fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "db3838a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATASET = \"data/processed/wosac/validation_json_100\"\n",
+    "FIGURES_DIR = \"examples/eval/figures\"\n",
+    "DATA_FOLDER = \"examples/eval/figures_data/\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2aaa50e1",
+   "metadata": {},
+   "source": [
+    "### Checking guidance data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "dce673ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load npy array from the file\n",
+    "FOLDER = \"examples/eval/figures_data/guidance/\"\n",
+    "\n",
+    "ref_log_replay = np.load(f\"{FOLDER}reference_log_replay.npy\")\n",
+    "ref_vbd_amortized = np.load(f\"{FOLDER}reference_vbd_amortized.npy\")\n",
+    "ref_vbd_online = np.load(f\"{FOLDER}reference_vbd_online.npy\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "c216b641",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((204, 91, 6), (204, 91, 6), (210, 91, 6))"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Should all be 91 \n",
+    "\n",
+    "# Controlled agent elements for 10 scenarios\n",
+    "ref_log_replay.shape, ref_vbd_amortized.shape, ref_vbd_online.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "a706660b",
+   "metadata": {},
+   "outputs": [
+    {
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+      "text/plain": [
+       "<Figure size 1300x300 with 3 Axes>"
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+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(13, 3),)# sharex=True, sharey=True)\n",
+    "\n",
+    "axs[0].hist(ref_log_replay[:, :, :2].flatten(), color='b', alpha=0.5, bins=20, label=\"log_replay\")\n",
+    "axs[1].hist(ref_vbd_amortized[:, :, :2].flatten(), color='r', alpha=1.0, bins=20, label=\"vbd_amortized\")\n",
+    "axs[2].hist(ref_vbd_online[:, :, :2].flatten(), color='g', alpha=1.0, bins=20, label=\"vbd_online\")\n",
+    "\n",
+    "axs[0].set_xlabel(\"Demeaned position in global coord. frame\")\n",
+    "axs[0].set_ylabel(\"Count\")\n",
+    "\n",
+    "fig.legend()\n",
+    "sns.despine()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "a08ab3a2",
+   "metadata": {},
+   "outputs": [
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+      "text/plain": [
+       "<Figure size 1300x300 with 3 Axes>"
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+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(13, 3) )# sharex=True, sharey=True)\n",
+    "\n",
+    "axs[0].hist(ref_log_replay[:, :, 2:4].flatten(), color='b', alpha=0.5, bins=20, label=\"log_replay\")\n",
+    "axs[1].hist(ref_vbd_amortized[:, :, 2:4].flatten(), color='r', alpha=1.0, bins=20, label=\"vbd_amortized\")\n",
+    "axs[2].hist(ref_vbd_online[:, :, 2:4].flatten(), color='g', alpha=1.0, bins=20, label=\"vbd_online\")\n",
+    "\n",
+    "axs[0].set_xlabel(\"Demeaned velocity [m/s]\")\n",
+    "axs[0].set_ylabel(\"Count\")\n",
+    "\n",
+    "fig.legend()\n",
+    "sns.despine()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "920285ab",
+   "metadata": {},
+   "outputs": [
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+       "   <rect x=\"574.336246\" y=\"27.345\" width=\"213.352941\" height=\"166.32\"/>\n",
+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 1300x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(13, 3),)# sharex=True, sharey=True)\n",
+    "\n",
+    "axs[0].hist(ref_log_replay[:, :, 4].flatten(), color='b', alpha=0.5, bins=20, label=\"log_replay\")\n",
+    "axs[1].hist(ref_vbd_amortized[:, :, 4].flatten(), color='r', alpha=1.0, bins=20, label=\"vbd_amortized\")\n",
+    "axs[2].hist(ref_vbd_online[:, :, 4].flatten(), color='g', alpha=1.0, bins=20, label=\"vbd_online\")\n",
+    "\n",
+    "axs[0].set_xlabel(\"Headings in global coord. frame [rad]\")\n",
+    "axs[0].set_ylabel(\"Count\")\n",
+    "\n",
+    "# add legend\n",
+    "fig.legend()\n",
+    "sns.despine()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "47af0e1f",
+   "metadata": {},
+   "outputs": [
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+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 1200x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for agent_idx in range(3):\n",
+    "    \n",
+    "    valid = ref_log_replay[agent_idx, :, -1].astype( bool)\n",
+    "\n",
+    "    fig, axs = plt.subplots(1, 3, figsize=(12, 4))\n",
+    "    axs[0].set_title(f\"Global position (x, y); agent {agent_idx}\")\n",
+    "    axs[0].plot(ref_log_replay[agent_idx, :, 0][valid], ref_log_replay[agent_idx, :, 1][valid], color='b')\n",
+    "    #axs[0].plot(ref_vbd_online[agent_idx, :, 0], ref_vbd_online[agent_idx, :, 1], color='g', alpha=0.5)\n",
+    "    axs[0].plot(ref_vbd_amortized[agent_idx, :, 0], ref_vbd_amortized[agent_idx, :, 1], color='r')\n",
+    "    axs[0].set_xlabel(\"x\")\n",
+    "    axs[0].set_ylabel(\"y\")\n",
+    "\n",
+    "    axs[1].set_title(f\"Global velocity (x, y); agent {agent_idx}\")\n",
+    "    axs[1].plot(ref_log_replay[agent_idx, :, 2][valid], ref_log_replay[agent_idx, :, 3][valid], color='b')\n",
+    "    axs[1].plot(ref_vbd_online[agent_idx, :, 2], ref_vbd_online[agent_idx, :, 3], color='g')\n",
+    "    axs[1].plot(ref_vbd_amortized[agent_idx, :, 2], ref_vbd_amortized[agent_idx, :, 3], color='r')\n",
+    "    axs[2].set_xlabel(r\"$v_x$\")\n",
+    "    axs[2].set_ylabel(r\"$v_y$\")\n",
+    "\n",
+    "    axs[2].set_title(f\"Global heading; agent {agent_idx}\")\n",
+    "    axs[2].plot(ref_log_replay[agent_idx, :, 4][valid], color='b', label=\"log_replay\")\n",
+    "    axs[2].plot(ref_vbd_online[agent_idx, :, 4], color='g', label=\"vbd_online\")\n",
+    "    axs[2].plot(ref_vbd_amortized[agent_idx, :, 4], color='r', label=\"vbd_amortized\")\n",
+    "    axs[2].set_ylabel(r\"$\\theta$\")\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "\n",
+    "    fig.legend(loc=\"center right\", facecolor=\"white\", bbox_to_anchor=(1.12, 0.5))\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "abac4ce3",
+   "metadata": {},
+   "source": [
+    "### Dataset stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "59043fd6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "valid_guidance_points = ref_log_replay[:, :, 5].sum(axis=-1)\n",
+    "valid_guidance_points.shape\n",
+    "\n",
+    "# Some trajectories, while valid, are just parked cars\n",
+    "# Detect these by checking for zero velocity trajectories\n",
+    "valid_and_non_zero = ((ref_log_replay[:, :, 2] != 0) & (ref_log_replay[:, :, 5] == 1)).sum(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "4708aa7f",
+   "metadata": {},
+   "outputs": [
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+       "   </g>\n",
+       "  </g>\n",
+       " </g>\n",
+       " <defs>\n",
+       "  <clipPath id=\"p26783bf7b4\">\n",
+       "   <rect x=\"47.591813\" y=\"43.776\" width=\"280.023\" height=\"156.636\"/>\n",
+       "  </clipPath>\n",
+       "  <clipPath id=\"p37d8812bd7\">\n",
+       "   <rect x=\"347.222812\" y=\"43.776\" width=\"280.023\" height=\"156.636\"/>\n",
+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 900x350 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 2, figsize=(9, 3.5), sharey=True)\n",
+    "\n",
+    "fig.suptitle(f\"Distribution of guidance points per episode; A = {ref_log_replay.shape[0]} (50 scenarios)\")\n",
+    "\n",
+    "sns.histplot(valid_guidance_points, bins=10, stat='percent', ax=axs[0])\n",
+    "axs[0].grid(True, alpha=0.2)\n",
+    "axs[0].set_xlabel(\"Guidance points per episode.\")\n",
+    "mean_value = valid_guidance_points.mean()\n",
+    "axs[0].axvline(x=mean_value, color='purple', linestyle='--')\n",
+    "axs[0].text(mean_value-3, axs[0].get_ylim()[1]*0.95, r'$\\mu$', color='purple', \n",
+    "            fontsize=14, ha='center', va='center')\n",
+    "\n",
+    "sns.histplot(valid_and_non_zero, bins=10, stat='percent', ax=axs[1])\n",
+    "axs[1].grid(True, alpha=0.2)\n",
+    "axs[1].set_xlabel(r\"$\\bf{Nonzero}$ guidance points per episode.\")\n",
+    "mean_value_nonzero = valid_and_non_zero.mean()\n",
+    "axs[1].axvline(x=mean_value_nonzero, color='purple', linestyle='--')\n",
+    "axs[1].text(mean_value_nonzero-3, axs[1].get_ylim()[1]*0.95, r'$\\mu$', color='purple', fontsize=14, ha='center', va='center')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "sns.despine()\n",
+    "plt.savefig(f\"{FIGURES_DIR}/guidance_points_distribution.pdf\", dpi=300, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2cd9d59d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09f01b81",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0ea8a40",
+   "metadata": {},
+   "source": [
+    "### Make guidance figs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee3b198a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# GUIDANCE_MODE = \"log_replay\"\n",
+    "\n",
+    "# env_config = EnvConfig(\n",
+    "#     reward_type=\"guided_autonomy\",\n",
+    "#     guidance=True,\n",
+    "#     guidance_mode=GUIDANCE_MODE,\n",
+    "#     add_reference_heading=True,\n",
+    "#     add_reference_speed=True,\n",
+    "#     add_reference_pos_xy=True,\n",
+    "#     init_mode=\"wosac_train\",\n",
+    "#     smoothen_trajectory=False,\n",
+    "#     dynamics_model=\"delta_local\",\n",
+    "#     guidance_dropout_prob=0.7,\n",
+    "#     init_steps=0,\n",
+    "# )\n",
+    "# render_config = RenderConfig()\n",
+    "\n",
+    "# train_loader = SceneDataLoader(\n",
+    "#     root=DATASET,\n",
+    "#     batch_size=1,\n",
+    "#     dataset_size=100,\n",
+    "#     sample_with_replacement=False,\n",
+    "#     shuffle=False,\n",
+    "#     file_prefix=\"\",\n",
+    "    \n",
+    "# )\n",
+    "\n",
+    "# env = GPUDriveTorchEnv(\n",
+    "#     config=env_config,\n",
+    "#     data_loader=train_loader,\n",
+    "#     max_cont_agents=32, \n",
+    "#     device=\"cpu\",\n",
+    "# )\n",
+    "\n",
+    "# print(env.data_batch)\n",
+    "\n",
+    "# obs = env.reset(env.cont_agent_mask)\n",
+    "# expert_actions, _, _, _ = env.get_expert_actions()\n",
+    "\n",
+    "# # for time_step in range(env.init_steps, 20):\n",
+    "# #     print(f\"Step: {env.step_in_world[0, 0, 0].item()}\")\n",
+    "\n",
+    "# #     # Step the environment\n",
+    "# #     expert_actions, _, _, _ = env.get_expert_actions()\n",
+    "# #     env.step_dynamics(expert_actions[:, :, time_step, :])\n",
+    "\n",
+    "# #     obs = env.get_obs(env.cont_agent_mask)\n",
+    "# #     rew = env.get_rewards()\n",
+    "    \n",
+    "\n",
+    "# # # Save for analysis\n",
+    "# # reference_traj = torch.cat([\n",
+    "# #     env.reference_trajectory.pos_xy,\n",
+    "# #     env.reference_trajectory.vel_xy,\n",
+    "# #     env.reference_trajectory.yaw,\n",
+    "# #     env.reference_trajectory.valids,\n",
+    "# # ], dim=-1)\n",
+    "# # reference_traj_np = reference_traj[env.cont_agent_mask].numpy()\n",
+    "\n",
+    "# # np.save(f\"reference_{GUIDANCE_MODE}.npy\", reference_traj_np)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43a62f4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "CENTER_AGENT_IDX = 0\n",
+    "\n",
+    "fig = env.vis.plot_agent_observation(\n",
+    "    env_idx=0,\n",
+    "    agent_idx=CENTER_AGENT_IDX,\n",
+    "    figsize=(10, 10),\n",
+    "    trajectory=env.reference_path[CENTER_AGENT_IDX, :, :],\n",
+    "    # step_reward=env.guidance_reward[\n",
+    "    #     0, 1\n",
+    "    # ].item(),\n",
+    "    # route_progress=env.route_progress[1],\n",
+    ")\n",
+    "\n",
+    "plt.savefig(\n",
+    "    os.path.join(FIGURES_DIR, f\"agent_observation_{CENTER_AGENT_IDX}_{GUIDANCE_MODE}.pdf\"),\n",
+    "    dpi=300,\n",
+    "    bbox_inches=\"tight\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4020148",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plot_agent_guidance_info(env.guidance_obs, center_agent_idx=1);\n",
+    "\n",
+    "plt.savefig(\n",
+    "    os.path.join(FIGURES_DIR, f\"agent_guidance_info_{CENTER_AGENT_IDX}_{GUIDANCE_MODE}.pdf\"),\n",
+    "    dpi=300,\n",
+    "    bbox_inches=\"tight\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82ac8a30",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "gpudrive",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/eval/notebooks/02_guidance_data_analysis.ipynb b/examples/eval/notebooks/02_guidance_data_analysis.ipynb
deleted file mode 100644
index 60eae77a4..000000000
--- a/examples/eval/notebooks/02_guidance_data_analysis.ipynb
+++ /dev/null
@@ -1,63838 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 1,
-=======
-   "execution_count": 52,
->>>>>>> dev
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import importlib\n",
-    "import gpudrive\n",
-    "importlib.reload(gpudrive)\n",
-    "\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import torch\n",
-    "from pathlib import Path\n",
-    "\n",
-    "# Set working directory to the base directory 'gpudrive_madrona'\n",
-    "working_dir = Path.cwd()\n",
-    "while working_dir.name != 'gpudrive':\n",
-    "    working_dir = working_dir.parent\n",
-    "    if working_dir == Path.home():\n",
-    "        raise FileNotFoundError(\"Base directory 'gpudrive_madrona' not found\")\n",
-    "os.chdir(working_dir)\n",
-    "import torch\n",
-    "from PIL import Image\n",
-    "import seaborn as sns\n",
-    "import warnings\n",
-    "import matplotlib as mpl\n",
-    "import matplotlib.pyplot as plt\n",
-    "import datetime\n",
-    "\n",
-    "from gpudrive.env.env_torch import GPUDriveTorchEnv\n",
-    "from gpudrive.env.config import EnvConfig, RenderConfig\n",
-    "from gpudrive.env.dataset import SceneDataLoader\n",
-    "from gpudrive.visualize.utils import img_from_fig\n",
-    "\n",
-    "sns.set(\"notebook\", font_scale=1.05, rc={\"figure.figsize\": (10, 5)})\n",
-    "sns.set_style(\"ticks\", rc={\"figure.facecolor\": \"none\", \"axes.facecolor\": \"none\"})\n",
-    "%config InlineBackend.figure_format = 'svg'\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "plt.set_loglevel(\"WARNING\")\n",
-    "mpl.rcParams[\"lines.markersize\"] = 8\n",
-    "\n",
-    "plt.set_loglevel(\"WARNING\")\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "MS = 9\n",
-    " \n",
-    "def plot_agent_guidance_info(guidance_obs, center_agent_idx):\n",
-    " \n",
-    "    agent_guidance = guidance_obs[center_agent_idx, :, :]\n",
-    "    mask = agent_guidance[:, 0] != -1.0\n",
-    "    \n",
-    "    time_steps = np.arange(agent_guidance.shape[0])\n",
-    "    \n",
-    "    fig, axes = plt.subplots(1, 3, figsize=(9.5, 3))\n",
-    "  \n",
-    "    axes[0].scatter(agent_guidance[:, 0][mask], agent_guidance[:, 1][mask], marker='o', s=MS, color='g')  \n",
-    "    axes[0].set_xlabel(r'Suggested $x$')\n",
-    "    axes[0].set_ylabel(r'Suggested $y$')\n",
-    "    #axes[0].legend()\n",
-    "    axes[0].grid(True, alpha=0.3)\n",
-    "    \n",
-    "    axes[1].scatter(time_steps[mask], agent_guidance[:, 2][mask], marker='o', color='b', s=MS)\n",
-    "    axes[1].set_xlabel('Time')\n",
-    "    axes[1].set_ylabel('Suggested speed')\n",
-    "    axes[1].grid(True, alpha=0.3)\n",
-    "    \n",
-    "    axes[2].scatter(time_steps[mask], agent_guidance[:, 3][mask], marker='o', color='#bc6c25', s=MS)\n",
-    "    axes[2].set_xlabel('Time')\n",
-    "    axes[2].set_ylabel('Suggested heading (rad)')\n",
-    "    axes[2].grid(True, alpha=0.3)\n",
-    "    \n",
-    "    #fig.suptitle(f\"Agent's {center_agent_idx} normalized guidance information\")\n",
-    "    \n",
-    "    plt.tight_layout()\n",
-    "    plt.subplots_adjust(top=0.85, bottom=0.15)\n",
-    "    \n",
-    "    return fig"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Settings"
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 3,
-=======
-   "execution_count": 53,
->>>>>>> dev
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "DATASET = \"data/processed/wosac/validation_json_100\"\n",
-    "FIGURES_DIR = \"examples/eval/figures\"\n",
-    "DATA_FOLDER = \"examples/eval/figures_data/\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Make environment"
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 4,
-=======
-   "execution_count": 39,
->>>>>>> dev
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['data/processed/wosac/validation_json_100/12858b7f79387840.json']\n"
-     ]
-    }
-   ],
-   "source": [
-    "GUIDANCE_MODE = \"log_replay\"\n",
-    "\n",
-    "env_config = EnvConfig(\n",
-    "    reward_type=\"guided_autonomy\",\n",
-    "    guidance=True,\n",
-    "    guidance_mode=GUIDANCE_MODE,\n",
-    "    add_reference_heading=True,\n",
-    "    add_reference_speed=True,\n",
-    "    add_reference_pos_xy=True,\n",
-    "    init_mode=\"wosac_train\",\n",
-    "    smoothen_trajectory=False,\n",
-    "    dynamics_model=\"delta_local\",\n",
-    "    guidance_dropout_prob=0.7,\n",
-    "    init_steps=0,\n",
-    ")\n",
-    "render_config = RenderConfig()\n",
-    "\n",
-    "train_loader = SceneDataLoader(\n",
-    "    root=DATASET,\n",
-    "    batch_size=1,\n",
-    "    dataset_size=100,\n",
-    "    sample_with_replacement=False,\n",
-    "    shuffle=False,\n",
-    "    file_prefix=\"\",\n",
-    "    \n",
-    ")\n",
-    "\n",
-    "env = GPUDriveTorchEnv(\n",
-    "    config=env_config,\n",
-    "    data_loader=train_loader,\n",
-    "    max_cont_agents=32, \n",
-    "    device=\"cpu\",\n",
-    ")\n",
-    "\n",
-    "print(env.data_batch)\n",
-    "\n",
-    "obs = env.reset(env.cont_agent_mask)\n",
-    "expert_actions, _, _, _ = env.get_expert_actions()\n",
-    "\n",
-    "# for time_step in range(env.init_steps, 20):\n",
-    "#     print(f\"Step: {env.step_in_world[0, 0, 0].item()}\")\n",
-    "\n",
-    "#     # Step the environment\n",
-    "#     expert_actions, _, _, _ = env.get_expert_actions()\n",
-    "#     env.step_dynamics(expert_actions[:, :, time_step, :])\n",
-    "\n",
-    "#     obs = env.get_obs(env.cont_agent_mask)\n",
-    "#     rew = env.get_rewards()\n",
-    "    \n",
-    "\n",
-    "# # Save for analysis\n",
-    "# reference_traj = torch.cat([\n",
-    "#     env.reference_trajectory.pos_xy,\n",
-    "#     env.reference_trajectory.vel_xy,\n",
-    "#     env.reference_trajectory.yaw,\n",
-    "#     env.reference_trajectory.valids,\n",
-    "# ], dim=-1)\n",
-    "# reference_traj_np = reference_traj[env.cont_agent_mask].numpy()\n",
-    "\n",
-    "# np.save(f\"reference_{GUIDANCE_MODE}.npy\", reference_traj_np)"
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([10, 2974])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "obs.shape"
-=======
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#env.reference_trajectory.pos_xy[0, 0, :, 0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Smoothening trajectory data"
->>>>>>> dev
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 6,
-=======
-   "execution_count": 42,
->>>>>>> dev
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim_states = env.vis.plot_simulator_state(\n",
-    "    env_indices=0,\n",
-    "    zoom_radius=60,\n",
-    "    plot_guidance_pos_xy=True,\n",
-    "    plot_guidance_up_to_time=False,\n",
-    "    center_agent_indices=[0],\n",
-    ")\n",
-    "fig = sim_states[0]\n",
-    "\n",
-    "fig.savefig(\n",
-    "    os.path.join(FIGURES_DIR, f\"wosac_adapted_sim_state.pdf\"),\n",
-    "    dpi=300,\n",
-    "    bbox_inches=\"tight\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
-       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
-       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
-       "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
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-    "CENTER_AGENT_IDX = 0\n",
-    "\n",
-    "fig = env.vis.plot_agent_observation(\n",
-    "    env_idx=0,\n",
-    "    agent_idx=CENTER_AGENT_IDX,\n",
-    "    figsize=(10, 10),\n",
-    "    trajectory=env.reference_path[CENTER_AGENT_IDX, :, :],\n",
-    "    # step_reward=env.guidance_reward[\n",
-    "    #     0, 1\n",
-    "    # ].item(),\n",
-    "    # route_progress=env.route_progress[1],\n",
-    ")\n",
-    "\n",
-    "plt.savefig(\n",
-    "    os.path.join(FIGURES_DIR, f\"agent_observation_{CENTER_AGENT_IDX}_{GUIDANCE_MODE}.pdf\"),\n",
-    "    dpi=300,\n",
-    "    bbox_inches=\"tight\",\n",
-    ")"
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-    "fig = plot_agent_guidance_info(env.guidance_obs, center_agent_idx=1);\n",
-    "\n",
-    "plt.savefig(\n",
-    "    os.path.join(FIGURES_DIR, f\"agent_guidance_info_{CENTER_AGENT_IDX}_{GUIDANCE_MODE}.pdf\"),\n",
-    "    dpi=300,\n",
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-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
-   "source": [
-    "env.reference_trajectory.pos_xy[0, 0, :, 0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Smoothening trajectory data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ref_log_replay_raw = np.load(f\"{DATA_FOLDER}reference_log_replay_raw.npy\")\n",
-    "ref_log_replay_smooth = np.load(f\"{DATA_FOLDER}reference_log_replay_smooth.npy\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-=======
-   "execution_count": 43,
->>>>>>> dev
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_trajectories(raw, smooth, agent_idx):\n",
-    "    \n",
-    "    valid = raw[agent_idx, :, 5].astype(bool)\n",
-    "    \n",
-    "    fig, axs = plt.subplots(1, 3, figsize=(10, 3))\n",
-    "    axs[0].set_title(f\"Global position (x, y); agent {agent_idx}\")\n",
-    "    axs[0].plot(raw[agent_idx, :, 0][valid], raw[agent_idx, :, 1][valid], color='b')\n",
-    "    axs[0].plot(smooth[agent_idx, :, 0][valid], smooth[agent_idx, :, 1][valid], color='orange')\n",
-    "    axs[0].set_xlabel(\"x\")\n",
-    "    axs[0].set_ylabel(\"y\")\n",
-    "\n",
-    "    axs[1].set_title(f\"Global velocity (x, y); agent {agent_idx}\")\n",
-    "    axs[1].plot(raw[agent_idx, :, 2][valid], raw[agent_idx, :, 3][valid], color='b')\n",
-    "    axs[1].plot(smooth[agent_idx, :, 2][valid], smooth[agent_idx, :, 3][valid], color='orange')\n",
-    "    axs[1].set_xlabel(r\"$v_x$\")\n",
-    "    axs[1].set_ylabel(r\"$v_y$\")\n",
-    "\n",
-    "    axs[2].set_title(f\"Global heading; agent {agent_idx}\")\n",
-    "    axs[2].plot(list(range(valid.sum())), raw[agent_idx, :, 4][valid], color='b', label=\"raw\")\n",
-    "    axs[2].plot(list(range(valid.sum())), smooth[agent_idx, :, 4][valid], color='orange', label=\"smoothed\")\n",
-    "    axs[2].set_ylabel(r\"$\\theta$\")\n",
-    "\n",
-    "    plt.tight_layout()\n",
-    "\n",
-    "    fig.legend(loc=\"center right\", facecolor=\"white\", bbox_to_anchor=(1.15, 0.5))\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
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-    "for i in range(5):\n",
-    "    plot_trajectories(raw=ref_log_replay_raw, smooth=ref_log_replay_smooth, agent_idx=i)"
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-    "## Aligning the scale of guidance data"
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-    "### Q1. Are the distributions from different modes (`vbd_online`, `log_replay` and `vbd_amortized`) on the same scale?\n",
-    "\n",
-    "- Unnormalized values\n",
-    "- Macroscopic (full dist)"
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-    "# Load npy array from the file\n",
-    "FOLDER = \"examples/eval/figures_data/\"\n",
-    "\n",
-    "ref_log_replay = np.load(f\"{DATA_FOLDER}reference_log_replay.npy\")\n",
-    "ref_vbd_amortized = np.load(f\"{DATA_FOLDER}reference_vbd_amortized.npy\")\n",
-    "ref_vbd_online = np.load(f\"{DATA_FOLDER}reference_vbd_online.npy\")"
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-    "# Controlled agent elements for 10 scenarios\n",
-    "ref_log_replay.shape, ref_vbd_amortized.shape, ref_vbd_online.shape"
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-    "### Q1 | **Global positions** $(x,y)$"
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-   "source": [
-    "fig, axs = plt.subplots(1, 3, figsize=(13, 3),)# sharex=True, sharey=True)\n",
-    "\n",
-    "axs[0].hist(ref_log_replay[:, :, :2].flatten(), color='b', alpha=0.5, bins=20, label=\"log_replay\")\n",
-    "axs[1].hist(ref_vbd_amortized[:, :, :2].flatten(), color='r', alpha=1.0, bins=20, label=\"vbd_amortized\")\n",
-    "axs[2].hist(ref_vbd_online[:, :, :2].flatten(), color='g', alpha=1.0, bins=20, label=\"vbd_online\")\n",
-    "\n",
-    "axs[0].set_xlabel(\"Demeaned position in global coord. frame\")\n",
-    "axs[0].set_ylabel(\"Count\")\n",
-    "\n",
-    "fig.legend()\n",
-    "sns.despine()"
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-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Q1 | **Velocities** $(x,y)$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
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-   "outputs": [
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-       "<Figure size 1300x300 with 3 Axes>"
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-   "source": [
-    "fig, axs = plt.subplots(1, 3, figsize=(13, 3) )# sharex=True, sharey=True)\n",
-    "\n",
-    "axs[0].hist(ref_log_replay[:, :, 2:4].flatten(), color='b', alpha=0.5, bins=20, label=\"log_replay\")\n",
-    "axs[1].hist(ref_vbd_amortized[:, :, 2:4].flatten(), color='r', alpha=1.0, bins=20, label=\"vbd_amortized\")\n",
-    "axs[2].hist(ref_vbd_online[:, :, 2:4].flatten(), color='g', alpha=1.0, bins=20, label=\"vbd_online\")\n",
-    "\n",
-    "axs[0].set_xlabel(\"Demeaned velocity [m/s]\")\n",
-    "axs[0].set_ylabel(\"Count\")\n",
-    "\n",
-    "fig.legend()\n",
-    "sns.despine()"
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-  },
-  {
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-   "metadata": {},
-   "source": [
-    "### Q1 | **Headings**"
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-   "execution_count": 50,
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-   "source": [
-    "fig, axs = plt.subplots(1, 3, figsize=(13, 3),)# sharex=True, sharey=True)\n",
-    "\n",
-    "axs[0].hist(ref_log_replay[:, :, 4].flatten(), color='b', alpha=0.5, bins=20, label=\"log_replay\")\n",
-    "axs[1].hist(ref_vbd_amortized[:, :, 4].flatten(), color='r', alpha=1.0, bins=20, label=\"vbd_amortized\")\n",
-    "axs[2].hist(ref_vbd_online[:, :, 4].flatten(), color='g', alpha=1.0, bins=20, label=\"vbd_online\")\n",
-    "\n",
-    "axs[0].set_xlabel(\"Headings in global coord. frame [rad]\")\n",
-    "axs[0].set_ylabel(\"Count\")\n",
-    "\n",
-    "# add legend\n",
-    "fig.legend()\n",
-    "sns.despine()"
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-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Q2. How do the trajectories differ qualitatively?\n",
-    "\n",
-    "- Plot several guidance trajectories for a particular agent to ensure that they are on the same scale"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
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-      "text/plain": [
-       "<Figure size 1200x400 with 3 Axes>"
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-    "for agent_idx in range(10):\n",
-    "    \n",
-    "    valid = ref_log_replay[agent_idx, :, -1].astype( bool)\n",
-    "\n",
-    "    fig, axs = plt.subplots(1, 3, figsize=(12, 4))\n",
-    "    axs[0].set_title(f\"Global position (x, y); agent {agent_idx}\")\n",
-    "    axs[0].plot(ref_log_replay[agent_idx, :, 0][valid], ref_log_replay[agent_idx, :, 1][valid], color='b')\n",
-    "    #axs[0].plot(ref_vbd_online[agent_idx, :, 0], ref_vbd_online[agent_idx, :, 1], color='g', alpha=0.5)\n",
-    "    axs[0].plot(ref_vbd_amortized[agent_idx, :, 0], ref_vbd_amortized[agent_idx, :, 1], color='r')\n",
-    "    axs[0].set_xlabel(\"x\")\n",
-    "    axs[0].set_ylabel(\"y\")\n",
-    "\n",
-    "    axs[1].set_title(f\"Global velocity (x, y); agent {agent_idx}\")\n",
-    "    axs[1].plot(ref_log_replay[agent_idx, :, 2][valid], ref_log_replay[agent_idx, :, 3][valid], color='b')\n",
-    "    axs[1].plot(ref_vbd_online[agent_idx, :, 2], ref_vbd_online[agent_idx, :, 3], color='g')\n",
-    "    axs[1].plot(ref_vbd_amortized[agent_idx, :, 2], ref_vbd_amortized[agent_idx, :, 3], color='r')\n",
-    "    axs[2].set_xlabel(r\"$v_x$\")\n",
-    "    axs[2].set_ylabel(r\"$v_y$\")\n",
-    "\n",
-    "    axs[2].set_title(f\"Global heading; agent {agent_idx}\")\n",
-    "    axs[2].plot(ref_log_replay[agent_idx, :, 4][valid], color='b', label=\"log_replay\")\n",
-    "    axs[2].plot(ref_vbd_online[agent_idx, :, 4], color='g', label=\"vbd_online\")\n",
-    "    axs[2].plot(ref_vbd_amortized[agent_idx, :, 4], color='r', label=\"vbd_amortized\")\n",
-    "    axs[2].set_ylabel(r\"$\\theta$\")\n",
-    "\n",
-    "    plt.tight_layout()\n",
-    "\n",
-    "    fig.legend(loc=\"center right\", facecolor=\"white\", bbox_to_anchor=(1.12, 0.5))\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Show corresponding full agent views"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
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-    "traj_masked = env.reference_path[center_agent_idx, :, :]\n",
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-    "traj_masked = env.reference_path[center_agent_idx, :, :]\n",
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-    "agent_obs_masked = env.vis.plot_agent_observation(\n",
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-    "center_agent_idx=0\n",
-    "\n",
-    "traj_masked = env.reference_path[center_agent_idx, :, :]\n",
-    "\n",
-    "agent_obs_masked = env.vis.plot_agent_observation(\n",
-    "    env_idx=0,\n",
-    "    agent_idx=center_agent_idx,\n",
-    "    figsize=(8, 8),\n",
-    "    trajectory=traj_masked,\n",
-    ")"
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-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Simulator state for that scene"
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-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
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-    "    env_indices=0,\n",
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-    "    plot_guidance_pos_xy=True,\n",
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-    "### Useful dataset statistics\n",
-    "\n",
-    "- What is the distribution of the guidance trajectories in the dataset?\n",
-    "    - What is the average length $T$?, what is the variance?\n",
-    "- How often is the guidance trajectory to \"do nothing\"?"
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-     "execution_count": 26,
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-    "ref_log_replay.shape # [agents, time, features]"
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-   "execution_count": 33,
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-    "valid_guidance_points = ref_log_replay[:, :, 5].sum(axis=-1)\n",
-    "valid_guidance_points.shape\n",
-    "\n",
-    "# Some trajectories, while valid, are just parked cars\n",
-    "# Detect these by checking for zero velocity trajectories\n",
-    "valid_and_non_zero = ((ref_log_replay[:, :, 2] != 0) & (ref_log_replay[:, :, 5] == 1)).sum(axis=1)"
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-       " </defs>\n",
-       "</svg>\n"
-      ],
-      "text/plain": [
-       "<Figure size 900x350 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axs = plt.subplots(1, 2, figsize=(9, 3.5), sharey=True)\n",
-    "\n",
-    "fig.suptitle(f\"Distribution of guidance points per episode; A = {ref_log_replay.shape[0]} (50 scenarios)\")\n",
-    "\n",
-    "sns.histplot(valid_guidance_points, bins=10, stat='percent', ax=axs[0])\n",
-    "axs[0].grid(True, alpha=0.2)\n",
-    "axs[0].set_xlabel(\"Guidance points per episode.\")\n",
-    "mean_value = valid_guidance_points.mean()\n",
-    "axs[0].axvline(x=mean_value, color='purple', linestyle='--')\n",
-    "axs[0].text(mean_value-3, axs[0].get_ylim()[1]*0.95, r'$\\mu$', color='purple', \n",
-    "            fontsize=14, ha='center', va='center')\n",
-    "\n",
-    "sns.histplot(valid_and_non_zero, bins=10, stat='percent', ax=axs[1])\n",
-    "axs[1].grid(True, alpha=0.2)\n",
-    "axs[1].set_xlabel(r\"$\\bf{Nonzero}$ guidance points per episode.\")\n",
-    "mean_value_nonzero = valid_and_non_zero.mean()\n",
-    "axs[1].axvline(x=mean_value_nonzero, color='purple', linestyle='--')\n",
-    "axs[1].text(mean_value_nonzero-3, axs[1].get_ylim()[1]*0.95, r'$\\mu$', color='purple', fontsize=14, ha='center', va='center')\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "sns.despine()\n",
-    "plt.savefig(f\"{FIGURES_DIR}/guidance_points_distribution.pdf\", dpi=300, bbox_inches=\"tight\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(69.444916, 30.798435, 91.0, 1.0)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "valid_guidance_points.mean(), valid_guidance_points.std(), valid_guidance_points.max(), valid_guidance_points.min()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "gpudrive",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/eval/notebooks/obtain_guidance_data.py b/examples/eval/notebooks/obtain_guidance_data.py
index f81b3c286..d70863863 100644
--- a/examples/eval/notebooks/obtain_guidance_data.py
+++ b/examples/eval/notebooks/obtain_guidance_data.py
@@ -11,9 +11,9 @@
 
 if __name__ == "__main__":
 
-    GUIDANCE_MODE = "log_replay"
+    GUIDANCE_MODE = "vbd_online"
     DATASET = "data/processed/wosac/validation_json_100"  # Ensure VBD trajectory structures are in here
-    SAVE_PATH = "examples/eval/figures_data/"
+    SAVE_PATH = "examples/eval/figures_data/guidance/"
 
     env_config = EnvConfig(
         dynamics_model="classic",
@@ -23,14 +23,14 @@
         add_reference_heading=True,
         add_reference_speed=True,
         add_reference_pos_xy=True,
-        init_mode="wosac_train",
+        init_mode="wosac_eval",
         smoothen_trajectory=False,
     )
     render_config = RenderConfig()
 
     train_loader = SceneDataLoader(
         root=DATASET,
-        batch_size=2,
+        batch_size=10,
         dataset_size=100,
         sample_with_replacement=False,
         shuffle=False,
@@ -40,7 +40,7 @@
     env = GPUDriveTorchEnv(
         config=env_config,
         data_loader=train_loader,
-        max_cont_agents=64,
+        max_cont_agents=32,
         device="cuda",
     )
 
diff --git a/examples/eval/run_wosac_eval.py b/examples/eval/run_wosac_eval.py
index ad589cabf..3ab2928a5 100644
--- a/examples/eval/run_wosac_eval.py
+++ b/examples/eval/run_wosac_eval.py
@@ -92,7 +92,7 @@ def rollout(
         info.collided_with_vehicle == 1
     ) & (info.type == int(madrona_gpudrive.EntityType.Vehicle))
     control_mask = control_mask_all & ~zero_action_mask
-    
+
     next_obs = env.reset(mask=control_mask)
 
     # Get scenario ids
@@ -143,7 +143,7 @@ def rollout(
             agent_observation_frames[idx].append(img_from_fig(agent_obs))
 
         # Get next observation
-        next_obs = env.get_obs(control_mask)          
+        next_obs = env.get_obs(control_mask)
         # NOTE(dc): Make sure to decouple the obs from the reward function
         reward = env.get_rewards()
         done = env.get_dones()
@@ -235,6 +235,7 @@ def rollout(
 
     return scenario_ids, scenario_rollouts, scenario_rollout_masks
 
+
 def load_config(config_path):
     """Load the configuration file."""
     with open(config_path, "r") as f:
@@ -252,9 +253,11 @@ def load_config(config_path):
     NUM_DATA_BATCHES = 1
     INIT_STEPS = 10
     DATASET_SIZE = 100
-    RENDER = False
+    RENDER = True
     LOG_DIR = "examples/eval/figures_data/wosac/"
-    GUIDANCE_MODE = "log_replay" #"vbd_amortized"
+    GUIDANCE_MODE = (
+        "log_replay"  # Options: "vbd_amortized", "vbd_online", "log_replay"
+    )
 
     DATA_JSON = "data/processed/wosac/validation_json_100"
     DATA_TFRECORD = "data/processed/wosac/validation_tfrecord_100"
@@ -269,16 +272,16 @@ def load_config(config_path):
         batch_size=NUM_ENVS,
         dataset_size=DATASET_SIZE,
         sample_with_replacement=True,
-        shuffle=False,
+        shuffle=True,
         file_prefix="",
     )
 
     # Load agent
     agent = load_agent(path_to_cpt=CPT_PATH).to(DEVICE)
-    
+
     # config = load_config("baselines/ppo/config/ppo_guided_autonomy.yaml")
     # config = config.environment
-    
+
     config = agent.config
 
     # Override default environment settings to match those the agent was trained with
@@ -304,25 +307,29 @@ def load_config(config_path):
         goal_behavior=config.goal_behavior,
         polyline_reduction_threshold=config.polyline_reduction_threshold,
         remove_non_vehicles=config.remove_non_vehicles,
-        lidar_obs=False, 
+        lidar_obs=False,
         obs_radius=config.obs_radius,
         max_steer_angle=config.max_steer_angle,
         max_accel_value=config.max_accel_value,
         action_space_steer_disc=config.action_space_steer_disc,
         action_space_accel_disc=config.action_space_accel_disc,
         # Override action space
-        steer_actions = torch.round(
+        steer_actions=torch.round(
             torch.linspace(
-                -config.max_steer_angle, config.max_steer_angle, config.action_space_steer_disc
+                -config.max_steer_angle,
+                config.max_steer_angle,
+                config.action_space_steer_disc,
             ),
             decimals=3,
+        ),
+        accel_actions=torch.round(
+            torch.linspace(
+                -config.max_accel_value,
+                config.max_accel_value,
+                config.action_space_accel_disc,
             ),
-        accel_actions = torch.round(
-                torch.linspace(
-                    -config.max_accel_value, config.max_accel_value, config.action_space_accel_disc
-                ),
-                decimals=3,
-            ),
+            decimals=3,
+        ),
         init_mode="wosac_eval",
         init_steps=INIT_STEPS,
         guidance_mode=GUIDANCE_MODE,
diff --git a/examples/eval/wosac_eval_origin.py b/examples/eval/wosac_eval_origin.py
index fa4364b69..fcf4d50c8 100644
--- a/examples/eval/wosac_eval_origin.py
+++ b/examples/eval/wosac_eval_origin.py
@@ -55,7 +55,7 @@ def __init__(
         else:
             self.baselines_df = baselines_df
 
-        self.timestamp = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
+        self.timestamp = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M")
 
         self.field_names = [
             "metametric",
diff --git a/gpudrive/datatypes/trajectory.py b/gpudrive/datatypes/trajectory.py
index 30881eed5..7d50b8309 100644
--- a/gpudrive/datatypes/trajectory.py
+++ b/gpudrive/datatypes/trajectory.py
@@ -228,7 +228,6 @@ def __init__(self, vbd_traj_tensor: torch.Tensor):
             torch.int32
         )
         
-
     @classmethod
     def from_tensor(
         cls,
diff --git a/gpudrive/env/env_torch.py b/gpudrive/env/env_torch.py
index dd91d6a09..1b3463b15 100755
--- a/gpudrive/env/env_torch.py
+++ b/gpudrive/env/env_torch.py
@@ -190,7 +190,7 @@ def setup_guidance(self):
                 self.max_agent_count,
                 madrona_gpudrive.kTrajectoryLength,
                 6,
-            )
+            ).to(self.device)
             reference_trajectory[
                 :, :, : self.init_steps + 1, :2
             ] = log_trajectory.pos_xy[:, :, : self.init_steps + 1]
diff --git a/src/dynamics.hpp b/src/dynamics.hpp
index 7fba26845..ed2fead12 100755
--- a/src/dynamics.hpp
+++ b/src/dynamics.hpp
@@ -25,9 +25,9 @@ namespace madrona_gpudrive
 
         float speed = velocity.linear.length();
         float yaw = utils::quatToYaw(rotation);
-        float x_dot = speed * cosf(utils::AngleAdd(yaw, velocity.angular.z));
-        float y_dot = speed * sinf(utils::AngleAdd(yaw, velocity.angular.z));
-        float theta_dot = speed * tanf(velocity.angular.z) / size.length;
+        float x_dot = speed * cosf(yaw);
+        float y_dot = speed * sinf(yaw);
+        float theta_dot = speed * tanf(velocity.angular.z) / (0.8 * size.length);
         float delta_dot = action.classic.steering;
         // Update the yaw
         float new_yaw = utils::AngleAdd(yaw, theta_dot * dt);

From 473e3e81c18c65a14f9096c5c4da71b82fd3ddef Mon Sep 17 00:00:00 2001
From: Kevin Joseph <kevinwinston184@gmail.com>
Date: Wed, 14 May 2025 20:23:26 -0400
Subject: [PATCH 16/38] wosac dataset (#448)

* dataset script

* wosac dataset script

* parallelized wosac script

* pkl support

* vbd cuda

---------
---
 .gitignore                                 |   1 +
 data_utils/process_waymo_files.py          |   4 +-
 data_utils/wosac_dataset.py                | 269 +++++++++++++++++++++
 gpudrive/env/env_torch.py                  |  18 +-
 gpudrive/integrations/vbd/data/amortize.py |  16 +-
 test_scenario_ids.pkl                      | Bin 0 -> 9487 bytes
 6 files changed, 293 insertions(+), 15 deletions(-)
 create mode 100644 data_utils/wosac_dataset.py
 create mode 100644 test_scenario_ids.pkl

diff --git a/.gitignore b/.gitignore
index eed9b1fe7..c5d73e5ce 100644
--- a/.gitignore
+++ b/.gitignore
@@ -27,6 +27,7 @@ data/raw/*
 data/processed/validation/*
 data/processed/training/*
 data/processed/testing/*
+data/processed/validation_interactive/*
 data/processed/pop_play/*
 data/processed/hand_designed/*
 figures/
diff --git a/data_utils/process_waymo_files.py b/data_utils/process_waymo_files.py
index fa3bdc2d4..6bf3f8936 100644
--- a/data_utils/process_waymo_files.py
+++ b/data_utils/process_waymo_files.py
@@ -630,9 +630,11 @@ def process_data(args):
         datasets = ["validation"]
     elif args.dataset == "testing":
         datasets = ["testing"]
+    elif args.dataset == "validation_interactive":
+        datasets = ["validation_interactive"]
     else:
         raise ValueError(
-            "Invalid dataset name. Must be one of: 'all', 'train', 'validation', or 'testing'"
+            "Invalid dataset name. Must be one of: 'all', 'train', 'validation', 'testing', or 'validation_interactive'."
         )
 
     for dataset in datasets:
diff --git a/data_utils/wosac_dataset.py b/data_utils/wosac_dataset.py
new file mode 100644
index 000000000..911c9254f
--- /dev/null
+++ b/data_utils/wosac_dataset.py
@@ -0,0 +1,269 @@
+import os
+# Force JAX to use CPU only
+os.environ["JAX_PLATFORMS"] = "cpu"
+
+import tensorflow as tf
+import glob
+import argparse
+import random
+import json
+import pickle  # Added for loading pickle files
+import multiprocessing
+from tqdm import tqdm
+from functools import partial
+from waymo_open_dataset.protos import scenario_pb2
+
+def extract_scene_from_tfrecord(tfrecord_path, scene_id, output_path):
+    """
+    Extract a specific scene from a TFRecord file based on its ID.
+    
+    Args:
+        tfrecord_path (str): Path to the TFRecord file
+        scene_id (str): ID of the scene to extract
+        output_path (str): Path to save the extracted scene
+    
+    Returns:
+        bool: True if scene was found and extracted, False otherwise
+    """
+    dataset = tf.data.TFRecordDataset(tfrecord_path, compression_type="")
+    found = False
+    
+    for tf_data in dataset:
+        tf_data_bytes = tf_data.numpy()
+        scenario = scenario_pb2.Scenario()
+        scenario.ParseFromString(bytes(tf_data_bytes))
+        
+        # Check if this is the scene we're looking for
+        if scenario.scenario_id == scene_id:
+            with tf.io.TFRecordWriter(output_path) as file_writer:
+                file_writer.write(tf_data_bytes)
+            found = True
+            break
+    
+    return found
+
+def process_single_scene(json_file, dataset, data_dir, tfrecord_dir, json_save_dir, tfrecord_save_dir):
+    """
+    Process a single scene file.
+    
+    Args:
+        json_file (str): Path to the JSON file
+        dataset (str): Name of the dataset
+        data_dir (str): Base directory containing JSON files
+        tfrecord_dir (str): Directory containing TFRecord files
+        json_save_dir (str): Directory to save processed JSON files
+        tfrecord_save_dir (str): Directory to save processed TFRecord files
+        
+    Returns:
+        tuple: (json_file, success, message)
+    """
+    try:
+        # Extract prefix and suffix from filename
+        filename = os.path.basename(json_file)
+        name_parts = os.path.splitext(filename)[0].split('_', 1)  # Split on the first underscore only
+        if len(name_parts) != 2:
+            return json_file, False, f"Invalid filename format: {filename}, expected <prefix>_<suffix>.json"
+        
+        prefix, suffix = name_parts
+        
+        # Find corresponding TFRecord file
+        tfrecord_path = os.path.join(tfrecord_dir, dataset, f"{dataset}.{prefix}")
+        
+        if not os.path.exists(tfrecord_path):
+            return json_file, False, f"TFRecord file not found: {tfrecord_path}"
+        
+        # Copy the JSON file to the output directory with new name
+        json_output_path = os.path.join(json_save_dir, f"{suffix}.json")
+        
+        # Copy the JSON file content
+        with open(json_file, 'r') as f_in:
+            json_data = json.load(f_in)
+        
+        with open(json_output_path, 'w') as f_out:
+            json.dump(json_data, f_out)
+        
+        # Extract the scene from the TFRecord file
+        tfrecord_output_path = os.path.join(tfrecord_save_dir, f"{suffix}.tfrecords")
+        found = extract_scene_from_tfrecord(tfrecord_path, suffix, tfrecord_output_path)
+        
+        if not found:
+            # Remove the JSON file if the TFRecord extraction failed
+            os.remove(json_output_path)
+            return json_file, False, f"Scene {suffix} not found in TFRecord file: {tfrecord_path}"
+        
+        return json_file, True, "Successfully processed"
+    
+    except Exception as e:
+        return json_file, False, f"Error processing {json_file}: {str(e)}"
+
+def find_json_by_suffix(data_path, suffix):
+    """
+    Find a JSON file with a specific suffix in a directory.
+    
+    Args:
+        data_path (str): Directory path to search in
+        suffix (str): Suffix to look for
+        
+    Returns:
+        str or None: Path to the JSON file if found, None otherwise
+    """
+    json_files = glob.glob(os.path.join(data_path, '*.json'))
+    
+    for json_file in json_files:
+        filename = os.path.basename(json_file)
+        name_parts = os.path.splitext(filename)[0].split('_', 1)
+        if len(name_parts) == 2 and name_parts[1] == suffix:
+            return json_file
+    
+    return None
+
+def process(dataset, num_scenes, data_dir, tfrecord_dir, save_dir, num_workers, scene_ids_pkl=None):
+    """
+    Process a specific dataset, selecting random scenes and extracting their TFRecords.
+    
+    Args:
+        dataset (str): Name of the dataset to process
+        num_scenes (int): Number of scenes to randomly select
+        data_dir (str): Directory containing JSON files
+        tfrecord_dir (str): Directory containing TFRecord files
+        save_dir (str): Directory to save processed files
+        num_workers (int): Number of worker processes to use
+        scene_ids_pkl (str): Path to pickle file containing scene IDs
+    """
+    # Set up paths
+    data_path = os.path.join(data_dir, dataset)
+    
+    # Create output directories
+    json_save_dir = os.path.join(save_dir, f"{dataset}", "json")
+    tfrecord_save_dir = os.path.join(save_dir, f"{dataset}", "tfrecord")
+    os.makedirs(json_save_dir, exist_ok=True)
+    os.makedirs(tfrecord_save_dir, exist_ok=True)
+    
+    selected_files = []
+    
+    # If pickle file with scene IDs is provided, use those IDs instead of random selection
+    if scene_ids_pkl:
+        try:
+            with open(scene_ids_pkl, 'rb') as f:
+                scene_ids = pickle.load(f)
+            
+            print(f"Loaded {len(scene_ids)} scene IDs from {scene_ids_pkl}")
+            
+            # Find corresponding JSON files for each scene ID
+            found_files = []
+            not_found_ids = []
+            
+            for suffix in scene_ids:
+                json_file = find_json_by_suffix(data_path, suffix)
+                if json_file:
+                    found_files.append(json_file)
+                else:
+                    not_found_ids.append(suffix)
+            
+            selected_files = found_files
+            
+            print(f"Found {len(selected_files)} matching JSON files, {len(not_found_ids)} scene IDs not found")
+            if not_found_ids and len(not_found_ids) < 10:
+                print(f"IDs not found: {', '.join(not_found_ids)}")
+            elif not_found_ids:
+                print(f"First 5 IDs not found: {', '.join(not_found_ids[:5])}, and {len(not_found_ids) - 5} more")
+                
+        except Exception as e:
+            print(f"Error loading scene IDs from {scene_ids_pkl}: {str(e)}")
+            return
+    else:
+        # Use random selection as before
+        json_files = glob.glob(os.path.join(data_path, '*.json'))
+        n_files = len(json_files)
+        
+        if n_files == 0:
+            print(f"No JSON files found in {data_path}")
+            return
+        
+        print(f"Found {n_files} files in {dataset} dataset")
+        num_to_select = min(num_scenes, n_files)
+        selected_files = random.sample(json_files, num_to_select)
+    
+    if not selected_files:
+        print(f"No files to process for {dataset}")
+        return
+    
+    print(f"Processing {len(selected_files)} files from {dataset} using {num_workers} workers")
+    
+    # Create a partial function with fixed arguments
+    process_func = partial(
+        process_single_scene,
+        dataset=dataset,
+        data_dir=data_dir,
+        tfrecord_dir=tfrecord_dir,
+        json_save_dir=json_save_dir,
+        tfrecord_save_dir=tfrecord_save_dir
+    )
+    
+    # Create a pool of workers and process the files
+    # Adjust number of workers to not exceed CPU count
+    num_workers = min(num_workers, multiprocessing.cpu_count(), len(selected_files))
+    
+    # If only one worker, process sequentially (avoiding multiprocessing overhead)
+    if num_workers == 1:
+        results = []
+        for json_file in tqdm(selected_files, desc=f"Processing {dataset}"):
+            results.append(process_func(json_file))
+    else:
+        # Use multiprocessing for parallel processing
+        with multiprocessing.Pool(num_workers) as pool:
+            results = list(tqdm(
+                pool.imap(process_func, selected_files),
+                total=len(selected_files),
+                desc=f"Processing {dataset}"
+            ))
+    
+    # Count successes and failures
+    successes = sum(1 for _, success, _ in results if success)
+    failures = len(results) - successes
+    
+    print(f"Finished processing {dataset}: {successes} succeeded, {failures} failed")
+    
+    # Print some failures if there are any (limit to 5 to avoid flooding console)
+    if failures > 0:
+        print("Some failures occurred:")
+        failure_count = 0
+        for _, success, message in results:
+            if not success:
+                print(f"  - {message}")
+                failure_count += 1
+                if failure_count >= 5 and failures > 5:
+                    print(f"  ... and {failures - 5} more failures")
+                    break
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data_dir', type=str, default='data/processed/')
+    parser.add_argument('--save_dir', type=str, default='data/processed/wosac/')
+    parser.add_argument('--tfrecord_dir', type=str, default='data/raw/')
+    parser.add_argument('--dataset', type=str, default='validation')
+    parser.add_argument('--num_scenes', type=int, default=1000)
+    parser.add_argument('--num_workers', type=int, default=multiprocessing.cpu_count())
+    parser.add_argument('--scene_ids_pkl', type=str, default=None, help='Path to pickle file containing scene IDs to process')
+    args = parser.parse_args()
+    
+    os.makedirs(args.save_dir, exist_ok=True)
+    
+    print(f'Processing data from {args.data_dir} and Saving to {args.save_dir}')
+    
+    if args.scene_ids_pkl:
+        print(f'Using scene IDs from {args.scene_ids_pkl}')
+    else:
+        print(f'Selecting {args.num_scenes} random scenes per dataset with {args.num_workers} workers')
+    
+    if args.dataset == 'all' or args.dataset == 'training':
+        process('training', args.num_scenes, args.data_dir, args.tfrecord_dir, args.save_dir, args.num_workers, args.scene_ids_pkl)
+
+    if args.dataset == 'all' or args.dataset == 'testing':
+        process('testing', args.num_scenes, args.data_dir, args.tfrecord_dir, args.save_dir, args.num_workers, args.scene_ids_pkl)
+            
+    if args.dataset == 'all' or args.dataset == 'validation':
+        process('validation', args.num_scenes, args.data_dir, args.tfrecord_dir, args.save_dir, args.num_workers, args.scene_ids_pkl)
+        
+    if args.dataset == 'all' or args.dataset == 'validation_interactive':
+        process('validation_interactive', args.num_scenes, args.data_dir, args.tfrecord_dir, args.save_dir, args.num_workers, args.scene_ids_pkl)
\ No newline at end of file
diff --git a/gpudrive/env/env_torch.py b/gpudrive/env/env_torch.py
index 1b3463b15..9db9717f6 100755
--- a/gpudrive/env/env_torch.py
+++ b/gpudrive/env/env_torch.py
@@ -303,7 +303,7 @@ def construct_context(self, init_steps=10):
                 - 'anchors': Reference points
         """
         means_xy = (
-            self.sim.world_means_tensor().to_torch()[:, :2].to(self.device)
+            self.sim.world_means_tensor().to_torch()[:, :2].to("cpu")
         )
 
         # Get the logged trajectory and restore the mean
@@ -312,7 +312,7 @@ def construct_context(self, init_steps=10):
             self.num_worlds,
             self.max_agent_count,
             backend=self.backend,
-            device=self.device,
+            device="cpu",
         )
         log_trajectory.restore_mean(
             mean_x=means_xy[:, 0], mean_y=means_xy[:, 1]
@@ -322,7 +322,7 @@ def construct_context(self, init_steps=10):
         global_road_graph = GlobalRoadGraphPoints.from_tensor(
             roadgraph_tensor=self.sim.map_observation_tensor(),
             backend=self.backend,
-            device=self.device,
+            device="cpu",
         )
         global_road_graph.restore_mean(
             mean_x=means_xy[:, 0], mean_y=means_xy[:, 1]
@@ -333,7 +333,7 @@ def construct_context(self, init_steps=10):
         global_agent_obs = GlobalEgoState.from_tensor(
             abs_self_obs_tensor=self.sim.absolute_self_observation_tensor(),
             backend=self.backend,
-            device=self.device,
+            device="cpu",
         )
         global_agent_obs.restore_mean(
             mean_x=means_xy[:, 0], mean_y=means_xy[:, 1]
@@ -341,7 +341,7 @@ def construct_context(self, init_steps=10):
         metadata = Metadata.from_tensor(
             metadata_tensor=self.sim.metadata_tensor(),
             backend=self.backend,
-            device=self.device,
+            device="cpu",
         )
         context_dict = process_scenario_data(
             max_controlled_agents=self.max_cont_agents,
@@ -351,10 +351,16 @@ def construct_context(self, init_steps=10):
             log_trajectory=log_trajectory,
             episode_len=self.episode_len,
             init_steps=init_steps,
-            raw_agent_types=self.sim.info_tensor().to_torch()[:, :, 4],
+            raw_agent_types=self.sim.info_tensor().to_torch().cpu()[:, :, 4],
             metadata=metadata,
         )
 
+        if (self.device != "cpu"):
+            # Move tensors to the specified device
+            for key in context_dict:
+                if isinstance(context_dict[key], torch.Tensor):
+                    context_dict[key] = context_dict[key].to(self.device)
+
         return context_dict
 
     def _set_reward_weights(self, condition_mode="random", agent_type=None):
diff --git a/gpudrive/integrations/vbd/data/amortize.py b/gpudrive/integrations/vbd/data/amortize.py
index 3f91ec946..b42b879a4 100644
--- a/gpudrive/integrations/vbd/data/amortize.py
+++ b/gpudrive/integrations/vbd/data/amortize.py
@@ -77,7 +77,7 @@ def main():
     args = parser.parse_args()
 
     # Always use CPU device
-    device = "cpu"
+    device = "cuda"
     MAX_CONTROLLED_AGENTS = 32
     print(f"Loading VBD model on {device}...")
     vbd_model = load_vbd_model(args.model_path, device, MAX_CONTROLLED_AGENTS)
@@ -125,7 +125,7 @@ def main():
         gpudrive_env.sim.expert_trajectory_tensor(),
         num_worlds=gpudrive_env.num_worlds,
         max_agents=gpudrive_env.max_agent_count,
-        device=device,
+        device="cpu",
     )
 
     output_trajectories[:, :, : INIT_STEPS + 1, :2] = log_trajectory.pos_xy[
@@ -153,7 +153,7 @@ def main():
 
     # World means for VBD outputs
     world_means = (
-        gpudrive_env.sim.world_means_tensor().to_torch()[:, :2].to(device)
+        gpudrive_env.sim.world_means_tensor().to_torch()[:, :2].to("cpu")
     )
 
     for _ in range(int(args.num_scenes / args.batch_size)):
@@ -162,11 +162,11 @@ def main():
             init_steps=gpudrive_env.init_steps
         )
         # Controlled agent mask
-        world_agent_indices = scene_context["agents_id"]
+        world_agent_indices = scene_context["agents_id"].to("cpu")
 
         # Generate VBD output
         predictions = vbd_model.sample_denoiser(scene_context)
-        vbd_output = predictions["denoised_trajs"].to(device).detach()
+        vbd_output = predictions["denoised_trajs"].to("cpu").detach()
 
         for i in range(gpudrive_env.num_worlds):
             # Get controlled agent indices for this world
@@ -205,13 +205,13 @@ def main():
 
         # Save to each file's json
         index_to_id = GlobalEgoState.from_tensor(
-            gpudrive_env.sim.absolute_self_observation_tensor(), device=device
+            gpudrive_env.sim.absolute_self_observation_tensor(), device="cpu"
         ).id
 
-        filenames = gpudrive_env.get_scenario_ids()
+        filenames = gpudrive_env.get_env_filenames()
 
         for i in range(gpudrive_env.num_worlds):
-            filename = f"{filenames[i]}.json"
+            filename = f"{filenames[i]}"
             with open(f"{args.input_dir}/{filename}", "r") as f:
                 data = json.load(f)
 
diff --git a/test_scenario_ids.pkl b/test_scenario_ids.pkl
new file mode 100644
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HcmV?d00001


From 23f91f44dc0f23cec2480253515dd1911fffcc7d Mon Sep 17 00:00:00 2001
From: Daphne Cornelisse <33460159+daphne-cornelisse@users.noreply.github.com>
Date: Wed, 14 May 2025 20:59:24 -0400
Subject: [PATCH 17/38] Intermediate update (#449)

* Fix: ensure vbd_online can be used on cuda and cpu.

* Small updates

* WOSAC defaults

* Dynamics model fix

* Minor

* Update sbatch

* Code to make latex wosac tables

* pkl file

* Add smoothness

* Add new dropout mode

* WOSAC eval improvements

* Add figures

* Increment progress based on visible route points only.

* Files

* Some small updates
---
 .gitignore                                    |    1 +
 baselines/ppo/config/ppo_guided_autonomy.yaml |    4 +-
 examples/eval/notebooks/00_playground.ipynb   |  566 ++
 .../eval/notebooks/01_wosac_analysis.ipynb    | 6786 +++++++++++++++++
 .../eval/notebooks/01_wosac_results.ipynb     | 3412 ---------
 .../notebooks/02_guidance_data_analyis.ipynb  | 2788 ++++++-
 .../eval/notebooks/04_guidance_density.ipynb  | 6045 +++++++++++++++
 examples/eval/rollout_test.py                 |    7 +-
 examples/eval/run_wosac_eval.py               |   58 +-
 examples/eval/wosac_eval_origin.py            |   42 +-
 gpudrive/env/config.py                        |    3 +-
 gpudrive/env/env_torch.py                     |  173 +-
 gpudrive/utils/generate_sbatch.py             |   25 +-
 13 files changed, 16331 insertions(+), 3579 deletions(-)
 create mode 100644 examples/eval/notebooks/00_playground.ipynb
 create mode 100644 examples/eval/notebooks/01_wosac_analysis.ipynb
 delete mode 100644 examples/eval/notebooks/01_wosac_results.ipynb
 create mode 100644 examples/eval/notebooks/04_guidance_density.ipynb

diff --git a/.gitignore b/.gitignore
index c5d73e5ce..9aabc7121 100644
--- a/.gitignore
+++ b/.gitignore
@@ -35,6 +35,7 @@ checkpoints/
 figures_data/
 data/other/*
 wosac/
+data/processed/validation_random/*
 
 # Logging
 /wandb
diff --git a/baselines/ppo/config/ppo_guided_autonomy.yaml b/baselines/ppo/config/ppo_guided_autonomy.yaml
index a9302f829..f7d134cb7 100644
--- a/baselines/ppo/config/ppo_guided_autonomy.yaml
+++ b/baselines/ppo/config/ppo_guided_autonomy.yaml
@@ -32,7 +32,7 @@ environment: # Overrides default environment configs (see pygpudrive/env/config.
   off_road_weight: -0.1
   guidance_speed_weight: 0.005
   guidance_heading_weight: 0.005
-  smoothness_weight: 0.0
+  smoothness_weight: 0.0001
 
   init_mode: wosac_train
   dynamics_model: "classic"
@@ -111,7 +111,7 @@ train:
   # # # Rendering # # #
   render: true # Determines whether to render the environment (note: will slow down training)
   render_3d: false # Render simulator state in 3d or 2d
-  render_interval: 400 # Render every k iterations
+  render_interval: 200 # Render every k iterations
   render_every_t: 5 # Render every k timesteps
   render_k_scenarios: 1 # Number of scenarios to render
   render_agent_idx: [0] # Agent observations to render
diff --git a/examples/eval/notebooks/00_playground.ipynb b/examples/eval/notebooks/00_playground.ipynb
new file mode 100644
index 000000000..99fb030cb
--- /dev/null
+++ b/examples/eval/notebooks/00_playground.ipynb
@@ -0,0 +1,566 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "017087fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "with open('/home/emerge/gpudrive/examples/eval/figures_data/test_scenario_ids.pkl', 'rb') as f:\n",
+    "    data = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "25236f84",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cefb2c10",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "gpudrive",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/eval/notebooks/01_wosac_analysis.ipynb b/examples/eval/notebooks/01_wosac_analysis.ipynb
new file mode 100644
index 000000000..a41539a10
--- /dev/null
+++ b/examples/eval/notebooks/01_wosac_analysis.ipynb
@@ -0,0 +1,6786 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os\n",
+    "import glob\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import importlib\n",
+    "import gpudrive\n",
+    "importlib.reload(gpudrive)\n",
+    "\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import torch\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# Set working directory to the base directory 'gpudrive_madrona'\n",
+    "working_dir = Path.cwd()\n",
+    "while working_dir.name != 'gpudrive':\n",
+    "    working_dir = working_dir.parent\n",
+    "    if working_dir == Path.home():\n",
+    "        raise FileNotFoundError(\"Base directory 'gpudrive_madrona' not found\")\n",
+    "os.chdir(working_dir)\n",
+    "import torch\n",
+    "from PIL import Image\n",
+    "import seaborn as sns\n",
+    "import warnings\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import datetime\n",
+    "\n",
+    "from gpudrive.env.env_torch import GPUDriveTorchEnv\n",
+    "from gpudrive.env.config import EnvConfig, RenderConfig\n",
+    "from gpudrive.env.dataset import SceneDataLoader\n",
+    "from gpudrive.visualize.utils import img_from_fig\n",
+    "\n",
+    "sns.set(\"notebook\", font_scale=1.05, rc={\"figure.figsize\": (5, 5)})\n",
+    "sns.set_style(\"ticks\", rc={\"figure.facecolor\": \"none\", \"axes.facecolor\": \"none\"})\n",
+    "%config InlineBackend.figure_format = 'svg'\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "plt.set_loglevel(\"WARNING\")\n",
+    "mpl.rcParams[\"lines.markersize\"] = 8\n",
+    "\n",
+    "plt.set_loglevel(\"WARNING\")\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Helper functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def display_wosac_table(csv_file):\n",
+    "  \n",
+    "    df = pd.read_csv(csv_file)\n",
+    "    df = df.set_index(\"AGENT POLICY\")\n",
+    "    \n",
+    "    def heatmap_best_bold(s):\n",
+    "        higher_better_cols = [col for col in df.columns if '(↑)' in col]\n",
+    "        lower_better_cols = [col for col in df.columns if '(↓)' in col]\n",
+    "        \n",
+    "        styles = pd.DataFrame('', index=df.index, columns=df.columns)\n",
+    "        \n",
+    "        # For higher-better columns (green)\n",
+    "        for col in higher_better_cols:\n",
+    "            max_val = df[col].max()\n",
+    "            min_val = df[col].min()\n",
+    "            range_val = max_val - min_val\n",
+    "            \n",
+    "            for idx in df.index:\n",
+    "                val = df.loc[idx, col]\n",
+    "                if range_val > 0:\n",
+    "                    norm_val = (val - min_val) / range_val\n",
+    "                    styles.loc[idx, col] = f'background-color: rgba(0, 255, 0, {norm_val * 0.5})'\n",
+    "                if val == max_val:\n",
+    "                    styles.loc[idx, col] += '; font-weight: bold'\n",
+    "        \n",
+    "        # For lower-better columns (pastel red)\n",
+    "        for col in lower_better_cols:\n",
+    "            min_val = df[col].min()\n",
+    "            max_val = df[col].max()\n",
+    "            range_val = max_val - min_val\n",
+    "            \n",
+    "            for idx in df.index:\n",
+    "                val = df.loc[idx, col]\n",
+    "                if range_val > 0:\n",
+    "                    norm_val = 1 - ((val - min_val) / range_val)\n",
+    "                    # Using pastel red (255, 180, 180)\n",
+    "                    styles.loc[idx, col] = f'background-color: rgba(255, 180, 180, {norm_val * 0.5})'\n",
+    "                if val == min_val:\n",
+    "                    styles.loc[idx, col] += '; font-weight: bold'\n",
+    "        \n",
+    "        return styles\n",
+    "    \n",
+    "    # Format the numbers without modifying the dataframe\n",
+    "    styled_df = df.style.apply(heatmap_best_bold, axis=None).format(precision=3)\n",
+    "    \n",
+    "    return styled_df\n",
+    "\n",
+    "def create_radar_chart(data_dir='examples/eval/figures_data'):\n",
+    "    csv_files = glob.glob(os.path.join(data_dir, \"*.csv\"))\n",
+    "    \n",
+    "    if not csv_files:\n",
+    "        raise FileNotFoundError(f\"No CSV files found in {data_dir}\")\n",
+    "    \n",
+    "    most_recent_file = max(csv_files, key=os.path.getctime)\n",
+    "    df = pd.read_csv(most_recent_file)\n",
+    "    df = df.set_index(\"AGENT POLICY\")\n",
+    "    \n",
+    "    key_metrics = [\n",
+    "        \"COMPOSITE METRIC (↑)\", \n",
+    "        \"LINEAR SPEED (↑)\", \n",
+    "        \"ANG. SPEED (↑)\",\n",
+    "        \"COLLISION (↑)\",\n",
+    "        \"TTC (↑)\", \n",
+    "        \"OFFROAD (↑)\"\n",
+    "    ]\n",
+    "    \n",
+    "    radar_df = df[key_metrics].copy()\n",
+    "    for col in radar_df.columns:\n",
+    "        if '(↑)' in col:\n",
+    "            radar_df[col] = (radar_df[col] - radar_df[col].min()) / (radar_df[col].max() - radar_df[col].min())\n",
+    "        else:\n",
+    "            radar_df[col] = 1 - ((radar_df[col] - radar_df[col].min()) / (radar_df[col].max() - radar_df[col].min()))\n",
+    "    \n",
+    "    categories = [col.replace(' (↑)', '').replace(' (↓)', '') for col in key_metrics]\n",
+    "    N = len(categories)\n",
+    "    \n",
+    "    fig = plt.figure(figsize=(6, 5))\n",
+    "    ax = plt.subplot(111, polar=True)\n",
+    "    \n",
+    "    angles = [n / float(N) * 2 * np.pi for n in range(N)]\n",
+    "    angles += angles[:1]\n",
+    "    \n",
+    "    top_policies = df.sort_values(\"COMPOSITE METRIC (↑)\", ascending=False).head(5).index\n",
+    "    \n",
+    "    colors = plt.cm.tab10(np.linspace(0, 1, len(top_policies)))\n",
+    "    \n",
+    "    for i, policy in enumerate(top_policies):\n",
+    "        values = radar_df.loc[policy].values.flatten().tolist()\n",
+    "        values += values[:1]\n",
+    "        \n",
+    "        ax.plot(angles, values, 'o-', linewidth=2, color=colors[i], label=policy)\n",
+    "        ax.fill(angles, values, alpha=0.1, color=colors[i])\n",
+    "    \n",
+    "    plt.xticks(angles[:-1], categories, size=12)\n",
+    "    \n",
+    "    ax.set_rlabel_position(0)\n",
+    "    plt.yticks([0.25, 0.5, 0.75], [\"0.25\", \"0.5\", \"0.75\"], color=\"grey\", size=10)\n",
+    "    plt.ylim(0, 1)\n",
+    "    \n",
+    "    plt.legend(loc='upper right', bbox_to_anchor=(0.1, 0.1))\n",
+    "    plt.title('Key Metrics Comparison (Normalized)', size=15, y=1.1)\n",
+    "    \n",
+    "    return fig\n",
+    "\n",
+    "\n",
+    "def df_to_clean_latex(df, caption=\"\", label=\"\", scaling=0.99, precision=3, oracle_row_name=\"LOGGED ORACLE\", short_version=False):\n",
+    "    \"\"\"\n",
+    "    Convert a DataFrame to a clean LaTeX table with alternating row colors and the best values bolded.\n",
+    "    Can also create a shortened version with aggregated metrics.\n",
+    "    \n",
+    "    Parameters:\n",
+    "    -----------\n",
+    "    df : pandas.DataFrame\n",
+    "        The DataFrame to convert\n",
+    "    caption : str\n",
+    "        Table caption\n",
+    "    label : str\n",
+    "        Table reference label\n",
+    "    scaling : float\n",
+    "        Scaling factor for table width (0-1)\n",
+    "    precision : int\n",
+    "        Number of decimal places for numerical values\n",
+    "    oracle_row_name : str\n",
+    "        Name of the oracle row to exclude from best value determination and place at bottom\n",
+    "    short_version : bool\n",
+    "        Whether to create a shortened version with aggregated metrics\n",
+    "    \n",
+    "    Returns:\n",
+    "    --------\n",
+    "    str : LaTeX code for the table\n",
+    "    \"\"\"\n",
+    "    # Make a copy to avoid modifying the original\n",
+    "    df_copy = df.copy()\n",
+    "    \n",
+    "    # If short_version is True, create the aggregated metrics\n",
+    "    if short_version:\n",
+    "        # Create aggregated metrics\n",
+    "        df_copy[\"Kinematic metrics (↑)\"] = (\n",
+    "            df_copy[\"LINEAR SPEED (↑)\"] \n",
+    "            + df_copy[\"LINEAR ACCEL. (↑)\"]\n",
+    "            + df_copy[\"ANG. SPEED (↑)\"]\n",
+    "            + df_copy[\"ANG. ACCEL. (↑)\"]\n",
+    "        ) / 4\n",
+    "\n",
+    "        df_copy[\"Interactive metrics (↑)\"] = (\n",
+    "            df_copy[\"COLLISION (↑)\"] \n",
+    "            + df_copy[\"TTC (↑)\"]\n",
+    "            + df_copy[\"DIST. TO OBJ. (↑)\"]\n",
+    "        ) / 3\n",
+    "        \n",
+    "        df_copy[\"Map-based metrics (↑)\"] = (\n",
+    "            df_copy[\"DIST. TO ROAD EDGE (↑)\"] \n",
+    "            + df_copy[\"OFFROAD (↑)\"]\n",
+    "        ) / 2\n",
+    "        \n",
+    "        # Keep only the desired columns\n",
+    "        columns = [\n",
+    "            \"COMPOSITE METRIC (↑)\",\n",
+    "            \"Kinematic metrics (↑)\",\n",
+    "            \"Interactive metrics (↑)\",\n",
+    "            \"Map-based metrics (↑)\",\n",
+    "        ]\n",
+    "        df_copy = df_copy[columns]\n",
+    "    \n",
+    "    # Identify columns by their improvement direction\n",
+    "    higher_better_cols = [col for col in df_copy.columns if '(↑)' in col]\n",
+    "    lower_better_cols = [col for col in df_copy.columns if '(↓)' in col]\n",
+    "    neutral_cols = [col for col in df_copy.columns if '(↑)' not in col and '(↓)' not in col]\n",
+    "    \n",
+    "    # Get column headers without the arrows for LaTeX\n",
+    "    clean_columns = [col.replace('(↑)', '$\\\\uparrow$').replace('(↓)', '$\\\\downarrow$') for col in df_copy.columns]\n",
+    "    \n",
+    "    # Move oracle row to the bottom if it exists\n",
+    "    oracle_index = None\n",
+    "    df_without_oracle = df_copy.copy()\n",
+    "    \n",
+    "    # Find oracle row with case-insensitive match\n",
+    "    for idx in df_copy.index:\n",
+    "        if oracle_row_name.lower() in str(idx).lower():\n",
+    "            oracle_index = idx\n",
+    "            oracle_row = df_copy.loc[idx].copy()\n",
+    "            df_without_oracle = df_without_oracle.drop(idx)\n",
+    "            break\n",
+    "    \n",
+    "    # Calculate best values for each column (excluding oracle row)\n",
+    "    best_values = {}\n",
+    "    for col in df_copy.columns:\n",
+    "        try:\n",
+    "            numeric_values = pd.to_numeric(df_without_oracle[col], errors='coerce')\n",
+    "            \n",
+    "            if col in higher_better_cols:\n",
+    "                best_values[col] = numeric_values.max()\n",
+    "            elif col in lower_better_cols:\n",
+    "                best_values[col] = numeric_values.min()\n",
+    "            else:\n",
+    "                best_values[col] = None  # No best value for neutral columns\n",
+    "        except:\n",
+    "            best_values[col] = None\n",
+    "    \n",
+    "    # Start building LaTeX table\n",
+    "    latex_code = []\n",
+    "    latex_code.append(\"\\\\begin{table}[htbp]\")\n",
+    "    latex_code.append(\"\\\\centering\")\n",
+    "    latex_code.append(f\"\\\\caption{{{caption}}}\")\n",
+    "    latex_code.append(f\"\\\\label{{{label}}}\")\n",
+    "    \n",
+    "    # Add code for alternating row colors\n",
+    "    latex_code.append(\"\\\\definecolor{lightgray}{gray}{0.95}\")  # Define light gray color\n",
+    "    latex_code.append(\"\\\\rowcolors{2}{white}{lightgray}\")  # Start with white for first row after header\n",
+    "    \n",
+    "    latex_code.append(f\"\\\\resizebox{{{scaling}\\\\textwidth}}{{!}}{{\")\n",
+    "    \n",
+    "    # Table environment\n",
+    "    latex_code.append(\"\\\\begin{tabular}{l\" + \"c\" * len(df_copy.columns) + \"}\")\n",
+    "    latex_code.append(\"\\\\toprule\")\n",
+    "    \n",
+    "    # Header row\n",
+    "    header = \"Method & \" + \" & \".join(clean_columns) + \" \\\\\\\\\"\n",
+    "    latex_code.append(header)\n",
+    "    latex_code.append(\"\\\\midrule\")\n",
+    "    \n",
+    "    # Process regular rows first (excluding oracle)\n",
+    "    for i, idx in enumerate(df_without_oracle.index):\n",
+    "        row = [idx]  # Start with index\n",
+    "        \n",
+    "        for col in df_copy.columns:\n",
+    "            val = df_without_oracle.loc[idx, col]\n",
+    "            \n",
+    "            # Format value\n",
+    "            try:\n",
+    "                val_numeric = float(val)\n",
+    "                cell_text = f\"{val_numeric:.{precision}f}\"\n",
+    "            except (ValueError, TypeError):\n",
+    "                cell_text = str(val)\n",
+    "            \n",
+    "            # Check if this is the best value (needs to be numeric)\n",
+    "            is_best = False\n",
+    "            if best_values[col] is not None:\n",
+    "                try:\n",
+    "                    val_numeric = float(val)\n",
+    "                    # Round both values to avoid floating point comparison issues\n",
+    "                    rounded_val = round(val_numeric, precision+2)\n",
+    "                    rounded_best = round(best_values[col], precision+2)\n",
+    "                    \n",
+    "                    if rounded_val == rounded_best:\n",
+    "                        is_best = True\n",
+    "                except (ValueError, TypeError):\n",
+    "                    pass\n",
+    "            \n",
+    "            # Format the cell\n",
+    "            if is_best:\n",
+    "                cell = f\"\\\\textbf{{{cell_text}}}\"\n",
+    "            else:\n",
+    "                cell = cell_text\n",
+    "            \n",
+    "            row.append(cell)\n",
+    "        \n",
+    "        latex_code.append(\" & \".join(row) + \" \\\\\\\\\")\n",
+    "    \n",
+    "    # Add a midrule before oracle row if it exists\n",
+    "    if oracle_index is not None:\n",
+    "        latex_code.append(\"\\\\midrule\")\n",
+    "        \n",
+    "        # Process oracle row\n",
+    "        row = [oracle_index]\n",
+    "        \n",
+    "        for col in df_copy.columns:\n",
+    "            val = oracle_row[col]\n",
+    "            \n",
+    "            # Format value\n",
+    "            try:\n",
+    "                val_numeric = float(val)\n",
+    "                cell_text = f\"{val_numeric:.{precision}f}\"\n",
+    "            except (ValueError, TypeError):\n",
+    "                cell_text = str(val)\n",
+    "            \n",
+    "            row.append(cell_text)\n",
+    "        \n",
+    "        latex_code.append(\" & \".join(row) + \" \\\\\\\\\")\n",
+    "    \n",
+    "    # Complete table\n",
+    "    latex_code.append(\"\\\\bottomrule\")\n",
+    "    latex_code.append(\"\\\\end{tabular}\")\n",
+    "    latex_code.append(\"}\")  # Close resizebox\n",
+    "    latex_code.append(\"\\\\end{table}\")\n",
+    "    \n",
+    "    return \"\\n\".join(latex_code)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Settings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "FILE = \"examples/eval/figures_data/wosac/wosac_table_2025-05-13-15-39.csv\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make tables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>AGENT POLICY</th>\n",
+       "      <th>REPLAN RATE (Hz)</th>\n",
+       "      <th>LINEAR SPEED (↑)</th>\n",
+       "      <th>LINEAR ACCEL. (↑)</th>\n",
+       "      <th>ANG. SPEED (↑)</th>\n",
+       "      <th>ANG. ACCEL. (↑)</th>\n",
+       "      <th>DIST. TO OBJ. (↑)</th>\n",
+       "      <th>COLLISION (↑)</th>\n",
+       "      <th>TTC (↑)</th>\n",
+       "      <th>DIST. TO ROAD EDGE (↑)</th>\n",
+       "      <th>OFFROAD (↑)</th>\n",
+       "      <th>COMPOSITE METRIC (↑)</th>\n",
+       "      <th>ADE (↓)</th>\n",
+       "      <th>MINADE (↓)</th>\n",
+       "      <th>COLLISION RATE (↓)</th>\n",
+       "      <th>OFFROAD RATE (↓)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Logged oracle</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.476000</td>\n",
+       "      <td>0.47800</td>\n",
+       "      <td>0.578000</td>\n",
+       "      <td>0.69400</td>\n",
+       "      <td>0.476000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.883000</td>\n",
+       "      <td>0.715000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.819000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.028000</td>\n",
+       "      <td>0.111000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>VBD</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.359000</td>\n",
+       "      <td>0.36600</td>\n",
+       "      <td>0.420000</td>\n",
+       "      <td>0.52200</td>\n",
+       "      <td>0.368000</td>\n",
+       "      <td>0.934000</td>\n",
+       "      <td>0.815000</td>\n",
+       "      <td>0.651000</td>\n",
+       "      <td>0.879000</td>\n",
+       "      <td>0.720000</td>\n",
+       "      <td>2.257000</td>\n",
+       "      <td>1.474000</td>\n",
+       "      <td>0.036000</td>\n",
+       "      <td>0.152000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Guided self-play (ours)</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.490498</td>\n",
+       "      <td>0.39734</td>\n",
+       "      <td>0.363043</td>\n",
+       "      <td>0.49292</td>\n",
+       "      <td>0.301346</td>\n",
+       "      <td>0.657433</td>\n",
+       "      <td>0.818667</td>\n",
+       "      <td>0.668172</td>\n",
+       "      <td>0.767871</td>\n",
+       "      <td>0.638876</td>\n",
+       "      <td>1.430006</td>\n",
+       "      <td>1.430006</td>\n",
+       "      <td>0.136359</td>\n",
+       "      <td>0.219048</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Random agent</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.002000</td>\n",
+       "      <td>0.11600</td>\n",
+       "      <td>0.014000</td>\n",
+       "      <td>0.03400</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.735000</td>\n",
+       "      <td>0.148000</td>\n",
+       "      <td>0.191000</td>\n",
+       "      <td>0.144000</td>\n",
+       "      <td>50.739000</td>\n",
+       "      <td>50.706000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.613000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              AGENT POLICY REPLAN RATE (Hz)  LINEAR SPEED (↑)  \\\n",
+       "0            Logged oracle                -          0.476000   \n",
+       "1                      VBD            0.125          0.359000   \n",
+       "2  Guided self-play (ours)               10          0.490498   \n",
+       "3             Random agent               10          0.002000   \n",
+       "\n",
+       "   LINEAR ACCEL. (↑)  ANG. SPEED (↑)  ANG. ACCEL. (↑)  DIST. TO OBJ. (↑)  \\\n",
+       "0            0.47800        0.578000          0.69400           0.476000   \n",
+       "1            0.36600        0.420000          0.52200           0.368000   \n",
+       "2            0.39734        0.363043          0.49292           0.301346   \n",
+       "3            0.11600        0.014000          0.03400           0.000000   \n",
+       "\n",
+       "   COLLISION (↑)   TTC (↑)  DIST. TO ROAD EDGE (↑)  OFFROAD (↑)  \\\n",
+       "0       1.000000  0.883000                0.715000     1.000000   \n",
+       "1       0.934000  0.815000                0.651000     0.879000   \n",
+       "2       0.657433  0.818667                0.668172     0.767871   \n",
+       "3       0.000000  0.735000                0.148000     0.191000   \n",
+       "\n",
+       "   COMPOSITE METRIC (↑)    ADE (↓)  MINADE (↓)  COLLISION RATE (↓)  \\\n",
+       "0              0.819000   0.000000    0.000000            0.028000   \n",
+       "1              0.720000   2.257000    1.474000            0.036000   \n",
+       "2              0.638876   1.430006    1.430006            0.136359   \n",
+       "3              0.144000  50.739000   50.706000            1.000000   \n",
+       "\n",
+       "   OFFROAD RATE (↓)  \n",
+       "0          0.111000  \n",
+       "1          0.152000  \n",
+       "2          0.219048  \n",
+       "3          0.613000  "
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_wosac_long = pd.read_csv(FILE)\n",
+    "\n",
+    "df_wosac_long.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# radar_fig = create_radar_chart(data_dir='../figures_data')\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  background-color: rgba(255, 180, 180, 0.0);\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_5e016\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_5e016_level0_col0\" class=\"col_heading level0 col0\" >REPLAN RATE (Hz)</th>\n",
+       "      <th id=\"T_5e016_level0_col1\" class=\"col_heading level0 col1\" >LINEAR SPEED (↑)</th>\n",
+       "      <th id=\"T_5e016_level0_col2\" class=\"col_heading level0 col2\" >LINEAR ACCEL. (↑)</th>\n",
+       "      <th id=\"T_5e016_level0_col3\" class=\"col_heading level0 col3\" >ANG. SPEED (↑)</th>\n",
+       "      <th id=\"T_5e016_level0_col4\" class=\"col_heading level0 col4\" >ANG. ACCEL. (↑)</th>\n",
+       "      <th id=\"T_5e016_level0_col5\" class=\"col_heading level0 col5\" >DIST. TO OBJ. (↑)</th>\n",
+       "      <th id=\"T_5e016_level0_col6\" class=\"col_heading level0 col6\" >COLLISION (↑)</th>\n",
+       "      <th id=\"T_5e016_level0_col7\" class=\"col_heading level0 col7\" >TTC (↑)</th>\n",
+       "      <th id=\"T_5e016_level0_col8\" class=\"col_heading level0 col8\" >DIST. TO ROAD EDGE (↑)</th>\n",
+       "      <th id=\"T_5e016_level0_col9\" class=\"col_heading level0 col9\" >OFFROAD (↑)</th>\n",
+       "      <th id=\"T_5e016_level0_col10\" class=\"col_heading level0 col10\" >COMPOSITE METRIC (↑)</th>\n",
+       "      <th id=\"T_5e016_level0_col11\" class=\"col_heading level0 col11\" >ADE (↓)</th>\n",
+       "      <th id=\"T_5e016_level0_col12\" class=\"col_heading level0 col12\" >MINADE (↓)</th>\n",
+       "      <th id=\"T_5e016_level0_col13\" class=\"col_heading level0 col13\" >COLLISION RATE (↓)</th>\n",
+       "      <th id=\"T_5e016_level0_col14\" class=\"col_heading level0 col14\" >OFFROAD RATE (↓)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >AGENT POLICY</th>\n",
+       "      <th class=\"blank col0\" >&nbsp;</th>\n",
+       "      <th class=\"blank col1\" >&nbsp;</th>\n",
+       "      <th class=\"blank col2\" >&nbsp;</th>\n",
+       "      <th class=\"blank col3\" >&nbsp;</th>\n",
+       "      <th class=\"blank col4\" >&nbsp;</th>\n",
+       "      <th class=\"blank col5\" >&nbsp;</th>\n",
+       "      <th class=\"blank col6\" >&nbsp;</th>\n",
+       "      <th class=\"blank col7\" >&nbsp;</th>\n",
+       "      <th class=\"blank col8\" >&nbsp;</th>\n",
+       "      <th class=\"blank col9\" >&nbsp;</th>\n",
+       "      <th class=\"blank col10\" >&nbsp;</th>\n",
+       "      <th class=\"blank col11\" >&nbsp;</th>\n",
+       "      <th class=\"blank col12\" >&nbsp;</th>\n",
+       "      <th class=\"blank col13\" >&nbsp;</th>\n",
+       "      <th class=\"blank col14\" >&nbsp;</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_5e016_level0_row0\" class=\"row_heading level0 row0\" >Logged oracle</th>\n",
+       "      <td id=\"T_5e016_row0_col0\" class=\"data row0 col0\" >-</td>\n",
+       "      <td id=\"T_5e016_row0_col1\" class=\"data row0 col1\" >0.476</td>\n",
+       "      <td id=\"T_5e016_row0_col2\" class=\"data row0 col2\" >0.478</td>\n",
+       "      <td id=\"T_5e016_row0_col3\" class=\"data row0 col3\" >0.578</td>\n",
+       "      <td id=\"T_5e016_row0_col4\" class=\"data row0 col4\" >0.694</td>\n",
+       "      <td id=\"T_5e016_row0_col5\" class=\"data row0 col5\" >0.476</td>\n",
+       "      <td id=\"T_5e016_row0_col6\" class=\"data row0 col6\" >1.000</td>\n",
+       "      <td id=\"T_5e016_row0_col7\" class=\"data row0 col7\" >0.883</td>\n",
+       "      <td id=\"T_5e016_row0_col8\" class=\"data row0 col8\" >0.715</td>\n",
+       "      <td id=\"T_5e016_row0_col9\" class=\"data row0 col9\" >1.000</td>\n",
+       "      <td id=\"T_5e016_row0_col10\" class=\"data row0 col10\" >0.819</td>\n",
+       "      <td id=\"T_5e016_row0_col11\" class=\"data row0 col11\" >0.000</td>\n",
+       "      <td id=\"T_5e016_row0_col12\" class=\"data row0 col12\" >0.000</td>\n",
+       "      <td id=\"T_5e016_row0_col13\" class=\"data row0 col13\" >0.028</td>\n",
+       "      <td id=\"T_5e016_row0_col14\" class=\"data row0 col14\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_5e016_level0_row1\" class=\"row_heading level0 row1\" >VBD</th>\n",
+       "      <td id=\"T_5e016_row1_col0\" class=\"data row1 col0\" >0.125</td>\n",
+       "      <td id=\"T_5e016_row1_col1\" class=\"data row1 col1\" >0.359</td>\n",
+       "      <td id=\"T_5e016_row1_col2\" class=\"data row1 col2\" >0.366</td>\n",
+       "      <td id=\"T_5e016_row1_col3\" class=\"data row1 col3\" >0.420</td>\n",
+       "      <td id=\"T_5e016_row1_col4\" class=\"data row1 col4\" >0.522</td>\n",
+       "      <td id=\"T_5e016_row1_col5\" class=\"data row1 col5\" >0.368</td>\n",
+       "      <td id=\"T_5e016_row1_col6\" class=\"data row1 col6\" >0.934</td>\n",
+       "      <td id=\"T_5e016_row1_col7\" class=\"data row1 col7\" >0.815</td>\n",
+       "      <td id=\"T_5e016_row1_col8\" class=\"data row1 col8\" >0.651</td>\n",
+       "      <td id=\"T_5e016_row1_col9\" class=\"data row1 col9\" >0.879</td>\n",
+       "      <td id=\"T_5e016_row1_col10\" class=\"data row1 col10\" >0.720</td>\n",
+       "      <td id=\"T_5e016_row1_col11\" class=\"data row1 col11\" >2.257</td>\n",
+       "      <td id=\"T_5e016_row1_col12\" class=\"data row1 col12\" >1.474</td>\n",
+       "      <td id=\"T_5e016_row1_col13\" class=\"data row1 col13\" >0.036</td>\n",
+       "      <td id=\"T_5e016_row1_col14\" class=\"data row1 col14\" >0.152</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_5e016_level0_row2\" class=\"row_heading level0 row2\" >Guided self-play (ours)</th>\n",
+       "      <td id=\"T_5e016_row2_col0\" class=\"data row2 col0\" >10</td>\n",
+       "      <td id=\"T_5e016_row2_col1\" class=\"data row2 col1\" >0.490</td>\n",
+       "      <td id=\"T_5e016_row2_col2\" class=\"data row2 col2\" >0.397</td>\n",
+       "      <td id=\"T_5e016_row2_col3\" class=\"data row2 col3\" >0.363</td>\n",
+       "      <td id=\"T_5e016_row2_col4\" class=\"data row2 col4\" >0.493</td>\n",
+       "      <td id=\"T_5e016_row2_col5\" class=\"data row2 col5\" >0.301</td>\n",
+       "      <td id=\"T_5e016_row2_col6\" class=\"data row2 col6\" >0.657</td>\n",
+       "      <td id=\"T_5e016_row2_col7\" class=\"data row2 col7\" >0.819</td>\n",
+       "      <td id=\"T_5e016_row2_col8\" class=\"data row2 col8\" >0.668</td>\n",
+       "      <td id=\"T_5e016_row2_col9\" class=\"data row2 col9\" >0.768</td>\n",
+       "      <td id=\"T_5e016_row2_col10\" class=\"data row2 col10\" >0.639</td>\n",
+       "      <td id=\"T_5e016_row2_col11\" class=\"data row2 col11\" >1.430</td>\n",
+       "      <td id=\"T_5e016_row2_col12\" class=\"data row2 col12\" >1.430</td>\n",
+       "      <td id=\"T_5e016_row2_col13\" class=\"data row2 col13\" >0.136</td>\n",
+       "      <td id=\"T_5e016_row2_col14\" class=\"data row2 col14\" >0.219</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_5e016_level0_row3\" class=\"row_heading level0 row3\" >Random agent</th>\n",
+       "      <td id=\"T_5e016_row3_col0\" class=\"data row3 col0\" >10</td>\n",
+       "      <td id=\"T_5e016_row3_col1\" class=\"data row3 col1\" >0.002</td>\n",
+       "      <td id=\"T_5e016_row3_col2\" class=\"data row3 col2\" >0.116</td>\n",
+       "      <td id=\"T_5e016_row3_col3\" class=\"data row3 col3\" >0.014</td>\n",
+       "      <td id=\"T_5e016_row3_col4\" class=\"data row3 col4\" >0.034</td>\n",
+       "      <td id=\"T_5e016_row3_col5\" class=\"data row3 col5\" >0.000</td>\n",
+       "      <td id=\"T_5e016_row3_col6\" class=\"data row3 col6\" >0.000</td>\n",
+       "      <td id=\"T_5e016_row3_col7\" class=\"data row3 col7\" >0.735</td>\n",
+       "      <td id=\"T_5e016_row3_col8\" class=\"data row3 col8\" >0.148</td>\n",
+       "      <td id=\"T_5e016_row3_col9\" class=\"data row3 col9\" >0.191</td>\n",
+       "      <td id=\"T_5e016_row3_col10\" class=\"data row3 col10\" >0.144</td>\n",
+       "      <td id=\"T_5e016_row3_col11\" class=\"data row3 col11\" >50.739</td>\n",
+       "      <td id=\"T_5e016_row3_col12\" class=\"data row3 col12\" >50.706</td>\n",
+       "      <td id=\"T_5e016_row3_col13\" class=\"data row3 col13\" >1.000</td>\n",
+       "      <td id=\"T_5e016_row3_col14\" class=\"data row3 col14\" >0.613</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f1267be2810>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table_formatted = display_wosac_table(csv_file=FILE)\n",
+    "display(table_formatted);\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DETAILED TABLE:\n",
+      "\\begin{table}[htbp]\n",
+      "\\centering\n",
+      "\\caption{Per-component WOSAC metric results on 1,000 held-out test scenarios from the WOMD validation split.}\n",
+      "\\label{tab:wosac-detailed}\n",
+      "\\definecolor{lightgray}{gray}{0.95}\n",
+      "\\rowcolors{2}{white}{lightgray}\n",
+      "\\resizebox{0.99\\textwidth}{!}{\n",
+      "\\begin{tabular}{lccccccccccccccc}\n",
+      "\\toprule\n",
+      "Method & REPLAN RATE (Hz) & LINEAR SPEED $\\uparrow$ & LINEAR ACCEL. $\\uparrow$ & ANG. SPEED $\\uparrow$ & ANG. ACCEL. $\\uparrow$ & DIST. TO OBJ. $\\uparrow$ & COLLISION $\\uparrow$ & TTC $\\uparrow$ & DIST. TO ROAD EDGE $\\uparrow$ & OFFROAD $\\uparrow$ & COMPOSITE METRIC $\\uparrow$ & ADE $\\downarrow$ & MINADE $\\downarrow$ & COLLISION RATE $\\downarrow$ & OFFROAD RATE $\\downarrow$ \\\\\n",
+      "\\midrule\n",
+      "VBD & 0.125 & 0.359 & 0.366 & \\textbf{0.420} & \\textbf{0.522} & \\textbf{0.368} & \\textbf{0.934} & 0.815 & 0.651 & \\textbf{0.879} & \\textbf{0.720} & 2.257 & 1.474 & \\textbf{0.036} & \\textbf{0.152} \\\\\n",
+      "Guided self-play (ours) & 10.000 & \\textbf{0.490} & \\textbf{0.397} & 0.363 & 0.493 & 0.301 & 0.657 & \\textbf{0.819} & \\textbf{0.668} & 0.768 & 0.639 & \\textbf{1.430} & \\textbf{1.430} & 0.136 & 0.219 \\\\\n",
+      "Random agent & 10.000 & 0.002 & 0.116 & 0.014 & 0.034 & 0.000 & 0.000 & 0.735 & 0.148 & 0.191 & 0.144 & 50.739 & 50.706 & 1.000 & 0.613 \\\\\n",
+      "\\midrule\n",
+      "Logged oracle & - & 0.476 & 0.478 & 0.578 & 0.694 & 0.476 & 1.000 & 0.883 & 0.715 & 1.000 & 0.819 & 0.000 & 0.000 & 0.028 & 0.111 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "}\n",
+      "\\end{table}\n",
+      "\n",
+      "\n",
+      "SHORT TABLE:\n",
+      "\\begin{table}[htbp]\n",
+      "\\centering\n",
+      "\\caption{Per-category WOSAC metric results on 1,000 held-out test scenarios from the WOMD validation split.}\n",
+      "\\label{tab:wosac-short}\n",
+      "\\definecolor{lightgray}{gray}{0.95}\n",
+      "\\rowcolors{2}{white}{lightgray}\n",
+      "\\resizebox{0.99\\textwidth}{!}{\n",
+      "\\begin{tabular}{lcccc}\n",
+      "\\toprule\n",
+      "Method & Composite Metric $\\uparrow$ & Kinematic Metrics $\\uparrow$ & Interactive Metrics $\\uparrow$ & Map-Based Metrics $\\uparrow$ \\\\\n",
+      "\\midrule\n",
+      "VBD & \\textbf{0.720} & 0.417 & \\textbf{0.706} & \\textbf{0.765} \\\\\n",
+      "Guided self-play (ours) & 0.639 & \\textbf{0.436} & 0.592 & 0.718 \\\\\n",
+      "Random agent & 0.144 & 0.042 & 0.245 & 0.169 \\\\\n",
+      "\\midrule\n",
+      "Logged oracle & 0.819 & 0.556 & 0.786 & 0.857 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "}\n",
+      "\\end{table}\n"
+     ]
+    }
+   ],
+   "source": [
+    "def display_wosac_tables(csv_file, detailed_caption=\"Detailed Performance Metrics\", \n",
+    "                        detailed_label=\"tab:detailed\", short_caption=\"Aggregated Performance Metrics\", \n",
+    "                        short_label=\"tab:short\", precision=3):\n",
+    "    \"\"\"\n",
+    "    Generate both detailed and short versions of the WOSAC metrics table.\n",
+    "    \n",
+    "    Parameters:\n",
+    "    -----------\n",
+    "    csv_file : str\n",
+    "        Path to the CSV file with the metrics\n",
+    "    detailed_caption : str\n",
+    "        Caption for the detailed table\n",
+    "    detailed_label : str\n",
+    "        Label for the detailed table\n",
+    "    short_caption : str\n",
+    "        Caption for the short table\n",
+    "    short_label : str\n",
+    "        Label for the short table\n",
+    "    precision : int\n",
+    "        Number of decimal places for numerical values\n",
+    "    \n",
+    "    Returns:\n",
+    "    --------\n",
+    "    tuple : (detailed_latex_code, short_latex_code)\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    df = pd.read_csv(csv_file)\n",
+    "    df = df.set_index(\"AGENT POLICY\")\n",
+    "    \n",
+    "    detailed_latex = df_to_clean_latex(\n",
+    "        df, \n",
+    "        caption=detailed_caption,\n",
+    "        label=detailed_label,\n",
+    "        precision=precision,\n",
+    "        oracle_row_name=\"LOGGED ORACLE\",\n",
+    "        short_version=False\n",
+    "    )\n",
+    "    \n",
+    "    # Generate short table\n",
+    "    # First, create the aggregated metrics\n",
+    "    df_short = df.copy()\n",
+    "    df_short[\"Kinematic metrics (↑)\"] = (\n",
+    "        df_short[\"LINEAR SPEED (↑)\"] \n",
+    "        + df_short[\"LINEAR ACCEL. (↑)\"]\n",
+    "        + df_short[\"ANG. SPEED (↑)\"]\n",
+    "        + df_short[\"ANG. ACCEL. (↑)\"]\n",
+    "    ) / 4\n",
+    "\n",
+    "    df_short[\"Interactive metrics (↑)\"] = (\n",
+    "        df_short[\"COLLISION (↑)\"] \n",
+    "        + df_short[\"TTC (↑)\"]\n",
+    "        + df_short[\"DIST. TO OBJ. (↑)\"]\n",
+    "    ) / 3\n",
+    "    \n",
+    "    df_short[\"Map-based metrics (↑)\"] = (\n",
+    "        df_short[\"DIST. TO ROAD EDGE (↑)\"] \n",
+    "        + df_short[\"OFFROAD (↑)\"]\n",
+    "    ) / 2\n",
+    "    \n",
+    "    # Keep only the desired columns\n",
+    "    columns = [\n",
+    "        \"COMPOSITE METRIC (↑)\",\n",
+    "        \"Kinematic metrics (↑)\",\n",
+    "        \"Interactive metrics (↑)\",\n",
+    "        \"Map-based metrics (↑)\",\n",
+    "    ]\n",
+    "    # Make lower case for column names\n",
+    "    df_short = df_short[columns]\n",
+    "    \n",
+    "    new_columns = [col.split('(')[0].strip().title() + (' (↑)' if '(↑)' in col else '') for col in columns]\n",
+    "    df_short.columns = new_columns\n",
+    "    \n",
+    "    # Generate the short table\n",
+    "    short_latex = df_to_clean_latex(\n",
+    "        df_short, \n",
+    "        caption=short_caption,\n",
+    "        label=short_label,\n",
+    "        precision=precision,\n",
+    "        oracle_row_name=\"LOGGED ORACLE\",\n",
+    "        short_version=False  # Already shortened the DataFrame\n",
+    "    )\n",
+    "    \n",
+    "    return detailed_latex, short_latex\n",
+    "\n",
+    "# Generate both tables\n",
+    "detailed_table, short_table = display_wosac_tables(\n",
+    "    csv_file=\"examples/eval/figures_data/wosac/wosac_table_2025-05-13-15-39.csv\",\n",
+    "    detailed_caption=\"Per-component WOSAC metric results on 1,000 held-out test scenarios from the WOMD validation split.\",\n",
+    "    detailed_label=\"tab:wosac-detailed\",\n",
+    "    short_caption=\"Per-category WOSAC metric results on 1,000 held-out test scenarios from the WOMD validation split.\",\n",
+    "    short_label=\"tab:wosac-short\"\n",
+    ")\n",
+    "\n",
+    "print(\"DETAILED TABLE:\")\n",
+    "print(detailed_table)\n",
+    "print(\"\\n\\nSHORT TABLE:\")\n",
+    "print(short_table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### WOSAC distributions score distributions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>scenario_id</th>\n",
+       "      <th>metametric</th>\n",
+       "      <th>average_displacement_error</th>\n",
+       "      <th>linear_speed_likelihood</th>\n",
+       "      <th>linear_acceleration_likelihood</th>\n",
+       "      <th>angular_speed_likelihood</th>\n",
+       "      <th>angular_acceleration_likelihood</th>\n",
+       "      <th>distance_to_nearest_object_likelihood</th>\n",
+       "      <th>collision_indication_likelihood</th>\n",
+       "      <th>time_to_collision_likelihood</th>\n",
+       "      <th>distance_to_road_edge_likelihood</th>\n",
+       "      <th>offroad_indication_likelihood</th>\n",
+       "      <th>min_average_displacement_error</th>\n",
+       "      <th>traffic_light_violation_likelihood</th>\n",
+       "      <th>simulated_collision_rate</th>\n",
+       "      <th>simulated_offroad_rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3f5c25d7205f8dff</td>\n",
+       "      <td>0.813539</td>\n",
+       "      <td>1.198634</td>\n",
+       "      <td>0.328827</td>\n",
+       "      <td>0.432671</td>\n",
+       "      <td>0.865604</td>\n",
+       "      <td>0.853203</td>\n",
+       "      <td>0.367522</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.554443</td>\n",
+       "      <td>0.957527</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>1.198634</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>83b48014b261a04b</td>\n",
+       "      <td>0.594855</td>\n",
+       "      <td>0.972785</td>\n",
+       "      <td>0.784031</td>\n",
+       "      <td>0.685769</td>\n",
+       "      <td>0.426380</td>\n",
+       "      <td>0.347577</td>\n",
+       "      <td>0.096663</td>\n",
+       "      <td>0.099867</td>\n",
+       "      <td>0.988889</td>\n",
+       "      <td>0.988889</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.972785</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>210b4bd10918f407</td>\n",
+       "      <td>0.816501</td>\n",
+       "      <td>0.985420</td>\n",
+       "      <td>0.370136</td>\n",
+       "      <td>0.387273</td>\n",
+       "      <td>0.384336</td>\n",
+       "      <td>0.686280</td>\n",
+       "      <td>0.285652</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.988889</td>\n",
+       "      <td>0.963881</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.985420</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>48f180b621e549ba</td>\n",
+       "      <td>0.569116</td>\n",
+       "      <td>2.670819</td>\n",
+       "      <td>0.427228</td>\n",
+       "      <td>0.363433</td>\n",
+       "      <td>0.248969</td>\n",
+       "      <td>0.701363</td>\n",
+       "      <td>0.284369</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.868735</td>\n",
+       "      <td>0.453082</td>\n",
+       "      <td>0.177606</td>\n",
+       "      <td>2.670819</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5eea5080e97ee2cf</td>\n",
+       "      <td>0.547978</td>\n",
+       "      <td>0.494265</td>\n",
+       "      <td>0.726353</td>\n",
+       "      <td>0.596820</td>\n",
+       "      <td>0.210328</td>\n",
+       "      <td>0.334826</td>\n",
+       "      <td>0.197285</td>\n",
+       "      <td>0.063008</td>\n",
+       "      <td>0.836675</td>\n",
+       "      <td>0.714255</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.494265</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.200000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        scenario_id  metametric  average_displacement_error  \\\n",
+       "0  3f5c25d7205f8dff    0.813539                    1.198634   \n",
+       "1  83b48014b261a04b    0.594855                    0.972785   \n",
+       "2  210b4bd10918f407    0.816501                    0.985420   \n",
+       "3  48f180b621e549ba    0.569116                    2.670819   \n",
+       "4  5eea5080e97ee2cf    0.547978                    0.494265   \n",
+       "\n",
+       "   linear_speed_likelihood  linear_acceleration_likelihood  \\\n",
+       "0                 0.328827                        0.432671   \n",
+       "1                 0.784031                        0.685769   \n",
+       "2                 0.370136                        0.387273   \n",
+       "3                 0.427228                        0.363433   \n",
+       "4                 0.726353                        0.596820   \n",
+       "\n",
+       "   angular_speed_likelihood  angular_acceleration_likelihood  \\\n",
+       "0                  0.865604                         0.853203   \n",
+       "1                  0.426380                         0.347577   \n",
+       "2                  0.384336                         0.686280   \n",
+       "3                  0.248969                         0.701363   \n",
+       "4                  0.210328                         0.334826   \n",
+       "\n",
+       "   distance_to_nearest_object_likelihood  collision_indication_likelihood  \\\n",
+       "0                               0.367522                         0.999002   \n",
+       "1                               0.096663                         0.099867   \n",
+       "2                               0.285652                         0.999002   \n",
+       "3                               0.284369                         0.999002   \n",
+       "4                               0.197285                         0.063008   \n",
+       "\n",
+       "   time_to_collision_likelihood  distance_to_road_edge_likelihood  \\\n",
+       "0                      0.554443                          0.957527   \n",
+       "1                      0.988889                          0.988889   \n",
+       "2                      0.988889                          0.963881   \n",
+       "3                      0.868735                          0.453082   \n",
+       "4                      0.836675                          0.714255   \n",
+       "\n",
+       "   offroad_indication_likelihood  min_average_displacement_error  \\\n",
+       "0                       0.999002                        1.198634   \n",
+       "1                       0.999002                        0.972785   \n",
+       "2                       0.999002                        0.985420   \n",
+       "3                       0.177606                        2.670819   \n",
+       "4                       0.999002                        0.494265   \n",
+       "\n",
+       "   traffic_light_violation_likelihood  simulated_collision_rate  \\\n",
+       "0                            0.999002                  0.000000   \n",
+       "1                            0.999002                  0.333333   \n",
+       "2                            0.999002                  0.000000   \n",
+       "3                            0.999002                  0.000000   \n",
+       "4                            0.999002                  0.000000   \n",
+       "\n",
+       "   simulated_offroad_rate  \n",
+       "0                0.000000  \n",
+       "1                0.666667  \n",
+       "2                0.000000  \n",
+       "3                0.250000  \n",
+       "4                0.200000  "
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_dist = pd.read_csv(\"examples/eval/figures_data/wosac/score_distribution_2025-05-13-15-39.csv\")\n",
+    "\n",
+    "df_dist.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
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+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 1200x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(12, 3))\n",
+    "\n",
+    "axs[0].hist(df_dist[\"metametric\"], bins=10)\n",
+    "axs[0].set_ylabel(\"Count\")\n",
+    "axs[0].set_xlabel(\"WOSAC composite metric\")\n",
+    "axs[0].grid(True, alpha=.3)\n",
+    "\n",
+    "axs[1].hist(df_dist[\"average_displacement_error\"], bins=10)\n",
+    "axs[1].set_ylabel(\"Count\")\n",
+    "axs[1].set_xlabel(\"Average displacement error\")\n",
+    "axs[1].grid(True, alpha=.3)\n",
+    "\n",
+    "axs[2].hist(df_dist[\"simulated_offroad_rate\"], bins=10)\n",
+    "axs[2].set_ylabel(\"Count\")\n",
+    "axs[2].set_xlabel(\"Offroad rate\")\n",
+    "axs[2].grid(True, alpha=.3)\n",
+    "\n",
+    "#sns.histplot(df_dist, x=\"metametric\", bins=50, kde=False)\n",
+    "\n",
+    "sns.despine()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='metametric', ylabel='simulated_offroad_rate'>"
+      ]
+     },
+     "execution_count": 143,
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+     "output_type": "execute_result"
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+       "<Figure size 500x500 with 1 Axes>"
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+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.regplot(data=df_dist, x='metametric', y='simulated_collision_rate')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "gpudrive",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
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+}
diff --git a/examples/eval/notebooks/01_wosac_results.ipynb b/examples/eval/notebooks/01_wosac_results.ipynb
deleted file mode 100644
index 0ae68570d..000000000
--- a/examples/eval/notebooks/01_wosac_results.ipynb
+++ /dev/null
@@ -1,3412 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import os\n",
-    "import glob\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import importlib\n",
-    "import gpudrive\n",
-    "importlib.reload(gpudrive)\n",
-    "\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import torch\n",
-    "from pathlib import Path\n",
-    "\n",
-    "# Set working directory to the base directory 'gpudrive_madrona'\n",
-    "working_dir = Path.cwd()\n",
-    "while working_dir.name != 'gpudrive':\n",
-    "    working_dir = working_dir.parent\n",
-    "    if working_dir == Path.home():\n",
-    "        raise FileNotFoundError(\"Base directory 'gpudrive_madrona' not found\")\n",
-    "os.chdir(working_dir)\n",
-    "import torch\n",
-    "from PIL import Image\n",
-    "import seaborn as sns\n",
-    "import warnings\n",
-    "import matplotlib as mpl\n",
-    "import matplotlib.pyplot as plt\n",
-    "import datetime\n",
-    "\n",
-    "from gpudrive.env.env_torch import GPUDriveTorchEnv\n",
-    "from gpudrive.env.config import EnvConfig, RenderConfig\n",
-    "from gpudrive.env.dataset import SceneDataLoader\n",
-    "from gpudrive.visualize.utils import img_from_fig\n",
-    "\n",
-    "sns.set(\"notebook\", font_scale=1.05, rc={\"figure.figsize\": (10, 5)})\n",
-    "sns.set_style(\"ticks\", rc={\"figure.facecolor\": \"none\", \"axes.facecolor\": \"none\"})\n",
-    "%config InlineBackend.figure_format = 'svg'\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "plt.set_loglevel(\"WARNING\")\n",
-    "mpl.rcParams[\"lines.markersize\"] = 8\n",
-    "\n",
-    "plt.set_loglevel(\"WARNING\")\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def display_wosac_table(data_dir):\n",
-    "    csv_files = glob.glob(os.path.join(data_dir, \"*.csv\"))\n",
-    "    \n",
-    "    if not csv_files:\n",
-    "        raise FileNotFoundError(f\"No CSV files found in {data_dir}\")\n",
-    "    \n",
-    "    most_recent_file = max(csv_files, key=os.path.getctime)\n",
-    "    print(f\"Using file: {most_recent_file}\")\n",
-    "    \n",
-    "    df = pd.read_csv(most_recent_file)\n",
-    "    df = df.set_index(\"AGENT POLICY\")\n",
-    "    \n",
-    "    def heatmap_best_bold(s):\n",
-    "        higher_better_cols = [col for col in df.columns if '(↑)' in col]\n",
-    "        lower_better_cols = [col for col in df.columns if '(↓)' in col]\n",
-    "        \n",
-    "        styles = pd.DataFrame('', index=df.index, columns=df.columns)\n",
-    "        \n",
-    "        # For higher-better columns (green)\n",
-    "        for col in higher_better_cols:\n",
-    "            max_val = df[col].max()\n",
-    "            min_val = df[col].min()\n",
-    "            range_val = max_val - min_val\n",
-    "            \n",
-    "            for idx in df.index:\n",
-    "                val = df.loc[idx, col]\n",
-    "                if range_val > 0:\n",
-    "                    norm_val = (val - min_val) / range_val\n",
-    "                    styles.loc[idx, col] = f'background-color: rgba(0, 255, 0, {norm_val * 0.5})'\n",
-    "                if val == max_val:\n",
-    "                    styles.loc[idx, col] += '; font-weight: bold'\n",
-    "        \n",
-    "        # For lower-better columns (pastel red)\n",
-    "        for col in lower_better_cols:\n",
-    "            min_val = df[col].min()\n",
-    "            max_val = df[col].max()\n",
-    "            range_val = max_val - min_val\n",
-    "            \n",
-    "            for idx in df.index:\n",
-    "                val = df.loc[idx, col]\n",
-    "                if range_val > 0:\n",
-    "                    norm_val = 1 - ((val - min_val) / range_val)\n",
-    "                    # Using pastel red (255, 180, 180)\n",
-    "                    styles.loc[idx, col] = f'background-color: rgba(255, 180, 180, {norm_val * 0.5})'\n",
-    "                if val == min_val:\n",
-    "                    styles.loc[idx, col] += '; font-weight: bold'\n",
-    "        \n",
-    "        return styles\n",
-    "    \n",
-    "    # Format the numbers without modifying the dataframe\n",
-    "    styled_df = df.style.apply(heatmap_best_bold, axis=None).format(precision=3)\n",
-    "    \n",
-    "    return styled_df\n",
-    "\n",
-    "def create_radar_chart(data_dir='examples/eval/figures_data'):\n",
-    "    csv_files = glob.glob(os.path.join(data_dir, \"*.csv\"))\n",
-    "    \n",
-    "    if not csv_files:\n",
-    "        raise FileNotFoundError(f\"No CSV files found in {data_dir}\")\n",
-    "    \n",
-    "    most_recent_file = max(csv_files, key=os.path.getctime)\n",
-    "    df = pd.read_csv(most_recent_file)\n",
-    "    df = df.set_index(\"AGENT POLICY\")\n",
-    "    \n",
-    "    key_metrics = [\n",
-    "        \"COMPOSITE METRIC (↑)\", \n",
-    "        \"LINEAR SPEED (↑)\", \n",
-    "        \"ANG. SPEED (↑)\",\n",
-    "        \"COLLISION (↑)\",\n",
-    "        \"TTC (↑)\", \n",
-    "        \"OFFROAD (↑)\"\n",
-    "    ]\n",
-    "    \n",
-    "    radar_df = df[key_metrics].copy()\n",
-    "    for col in radar_df.columns:\n",
-    "        if '(↑)' in col:\n",
-    "            radar_df[col] = (radar_df[col] - radar_df[col].min()) / (radar_df[col].max() - radar_df[col].min())\n",
-    "        else:\n",
-    "            radar_df[col] = 1 - ((radar_df[col] - radar_df[col].min()) / (radar_df[col].max() - radar_df[col].min()))\n",
-    "    \n",
-    "    categories = [col.replace(' (↑)', '').replace(' (↓)', '') for col in key_metrics]\n",
-    "    N = len(categories)\n",
-    "    \n",
-    "    fig = plt.figure(figsize=(6, 5))\n",
-    "    ax = plt.subplot(111, polar=True)\n",
-    "    \n",
-    "    angles = [n / float(N) * 2 * np.pi for n in range(N)]\n",
-    "    angles += angles[:1]\n",
-    "    \n",
-    "    top_policies = df.sort_values(\"COMPOSITE METRIC (↑)\", ascending=False).head(5).index\n",
-    "    \n",
-    "    colors = plt.cm.tab10(np.linspace(0, 1, len(top_policies)))\n",
-    "    \n",
-    "    for i, policy in enumerate(top_policies):\n",
-    "        values = radar_df.loc[policy].values.flatten().tolist()\n",
-    "        values += values[:1]\n",
-    "        \n",
-    "        ax.plot(angles, values, 'o-', linewidth=2, color=colors[i], label=policy)\n",
-    "        ax.fill(angles, values, alpha=0.1, color=colors[i])\n",
-    "    \n",
-    "    plt.xticks(angles[:-1], categories, size=12)\n",
-    "    \n",
-    "    ax.set_rlabel_position(0)\n",
-    "    plt.yticks([0.25, 0.5, 0.75], [\"0.25\", \"0.5\", \"0.75\"], color=\"grey\", size=10)\n",
-    "    plt.ylim(0, 1)\n",
-    "    \n",
-    "    plt.legend(loc='upper right', bbox_to_anchor=(0.1, 0.1))\n",
-    "    plt.title('Key Metrics Comparison (Normalized)', size=15, y=1.1)\n",
-    "    \n",
-    "    return fig"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using file: ../figures_data/wosac_table_2025-04-29 12.csv\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "#T_489db_row0_col4, #T_489db_row0_col6, #T_489db_row0_col7, #T_489db_row0_col8, #T_489db_row0_col9, #T_489db_row0_col10, #T_489db_row2_col1, #T_489db_row2_col2, #T_489db_row2_col3, #T_489db_row2_col5 {\n",
-       "  background-color: rgba(0, 255, 0, 0.5);\n",
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-       "#T_489db_row0_col5 {\n",
-       "  background-color: rgba(0, 255, 0, 0.3721843005938857);\n",
-       "}\n",
-       "#T_489db_row0_col11, #T_489db_row0_col12, #T_489db_row0_col13, #T_489db_row0_col14 {\n",
-       "  background-color: rgba(255, 180, 180, 0.5);\n",
-       "  font-weight: bold;\n",
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-       "#T_489db_row1_col1 {\n",
-       "  background-color: rgba(0, 255, 0, 0.2369363386202271);\n",
-       "}\n",
-       "#T_489db_row1_col2 {\n",
-       "  background-color: rgba(0, 255, 0, 0.3273525991163753);\n",
-       "}\n",
-       "#T_489db_row1_col3 {\n",
-       "  background-color: rgba(0, 255, 0, 0.3532274801016337);\n",
-       "}\n",
-       "#T_489db_row1_col4 {\n",
-       "  background-color: rgba(0, 255, 0, 0.36969696969696975);\n",
-       "}\n",
-       "#T_489db_row1_col5 {\n",
-       "  background-color: rgba(0, 255, 0, 0.28773912314821415);\n",
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-       "#T_489db_row1_col6 {\n",
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-       "#T_489db_row1_col7 {\n",
-       "  background-color: rgba(0, 255, 0, 0.2702702702702701);\n",
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-       "#T_489db_row1_col8 {\n",
-       "  background-color: rgba(0, 255, 0, 0.44356261022927695);\n",
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-       "#T_489db_row1_col9 {\n",
-       "  background-color: rgba(0, 255, 0, 0.4252163164400494);\n",
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-       "  background-color: rgba(0, 255, 0, 0.4266666666666667);\n",
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-       "  background-color: rgba(255, 180, 180, 0.4785068842674822);\n",
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-       "#T_489db_row2_col8 {\n",
-       "  background-color: rgba(0, 255, 0, 0.46655078581821774);\n",
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-       "#T_489db_row2_col9 {\n",
-       "  background-color: rgba(0, 255, 0, 0.08450589398076416);\n",
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-       "#T_489db_row2_col10 {\n",
-       "  background-color: rgba(0, 255, 0, 0.4025441141481753);\n",
-       "}\n",
-       "#T_489db_row2_col11, #T_489db_row2_col12, #T_489db_row3_col13, #T_489db_row3_col14 {\n",
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-       "#T_489db_row2_col13 {\n",
-       "  background-color: rgba(255, 180, 180, 0.45724737040177293);\n",
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-       "  background-color: rgba(0, 255, 0, 0.0);\n",
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-       "#T_489db_row3_col11 {\n",
-       "  background-color: rgba(255, 180, 180, 0.01681914082755065);\n",
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-       "</style>\n",
-       "<table id=\"T_489db\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_489db_level0_col0\" class=\"col_heading level0 col0\" >REPLAN RATE (Hz)</th>\n",
-       "      <th id=\"T_489db_level0_col1\" class=\"col_heading level0 col1\" >LINEAR SPEED (↑)</th>\n",
-       "      <th id=\"T_489db_level0_col2\" class=\"col_heading level0 col2\" >LINEAR ACCEL. (↑)</th>\n",
-       "      <th id=\"T_489db_level0_col3\" class=\"col_heading level0 col3\" >ANG. SPEED (↑)</th>\n",
-       "      <th id=\"T_489db_level0_col4\" class=\"col_heading level0 col4\" >ANG. ACCEL. (↑)</th>\n",
-       "      <th id=\"T_489db_level0_col5\" class=\"col_heading level0 col5\" >DIST. TO OBJ. (↑)</th>\n",
-       "      <th id=\"T_489db_level0_col6\" class=\"col_heading level0 col6\" >COLLISION (↑)</th>\n",
-       "      <th id=\"T_489db_level0_col7\" class=\"col_heading level0 col7\" >TTC (↑)</th>\n",
-       "      <th id=\"T_489db_level0_col8\" class=\"col_heading level0 col8\" >DIST. TO ROAD EDGE (↑)</th>\n",
-       "      <th id=\"T_489db_level0_col9\" class=\"col_heading level0 col9\" >OFFROAD (↑)</th>\n",
-       "      <th id=\"T_489db_level0_col10\" class=\"col_heading level0 col10\" >COMPOSITE METRIC (↑)</th>\n",
-       "      <th id=\"T_489db_level0_col11\" class=\"col_heading level0 col11\" >ADE (↓)</th>\n",
-       "      <th id=\"T_489db_level0_col12\" class=\"col_heading level0 col12\" >MINADE (↓)</th>\n",
-       "      <th id=\"T_489db_level0_col13\" class=\"col_heading level0 col13\" >COLLISION RATE (↓)</th>\n",
-       "      <th id=\"T_489db_level0_col14\" class=\"col_heading level0 col14\" >OFFROAD RATE (↓)</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >AGENT POLICY</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "      <th class=\"blank col5\" >&nbsp;</th>\n",
-       "      <th class=\"blank col6\" >&nbsp;</th>\n",
-       "      <th class=\"blank col7\" >&nbsp;</th>\n",
-       "      <th class=\"blank col8\" >&nbsp;</th>\n",
-       "      <th class=\"blank col9\" >&nbsp;</th>\n",
-       "      <th class=\"blank col10\" >&nbsp;</th>\n",
-       "      <th class=\"blank col11\" >&nbsp;</th>\n",
-       "      <th class=\"blank col12\" >&nbsp;</th>\n",
-       "      <th class=\"blank col13\" >&nbsp;</th>\n",
-       "      <th class=\"blank col14\" >&nbsp;</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_489db_level0_row0\" class=\"row_heading level0 row0\" >Logged oracle</th>\n",
-       "      <td id=\"T_489db_row0_col0\" class=\"data row0 col0\" >-</td>\n",
-       "      <td id=\"T_489db_row0_col1\" class=\"data row0 col1\" >0.476</td>\n",
-       "      <td id=\"T_489db_row0_col2\" class=\"data row0 col2\" >0.478</td>\n",
-       "      <td id=\"T_489db_row0_col3\" class=\"data row0 col3\" >0.578</td>\n",
-       "      <td id=\"T_489db_row0_col4\" class=\"data row0 col4\" >0.694</td>\n",
-       "      <td id=\"T_489db_row0_col5\" class=\"data row0 col5\" >0.476</td>\n",
-       "      <td id=\"T_489db_row0_col6\" class=\"data row0 col6\" >1.000</td>\n",
-       "      <td id=\"T_489db_row0_col7\" class=\"data row0 col7\" >0.883</td>\n",
-       "      <td id=\"T_489db_row0_col8\" class=\"data row0 col8\" >0.715</td>\n",
-       "      <td id=\"T_489db_row0_col9\" class=\"data row0 col9\" >1.000</td>\n",
-       "      <td id=\"T_489db_row0_col10\" class=\"data row0 col10\" >0.819</td>\n",
-       "      <td id=\"T_489db_row0_col11\" class=\"data row0 col11\" >0.000</td>\n",
-       "      <td id=\"T_489db_row0_col12\" class=\"data row0 col12\" >0.000</td>\n",
-       "      <td id=\"T_489db_row0_col13\" class=\"data row0 col13\" >0.028</td>\n",
-       "      <td id=\"T_489db_row0_col14\" class=\"data row0 col14\" >0.111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_489db_level0_row1\" class=\"row_heading level0 row1\" >VBD</th>\n",
-       "      <td id=\"T_489db_row1_col0\" class=\"data row1 col0\" >0.125</td>\n",
-       "      <td id=\"T_489db_row1_col1\" class=\"data row1 col1\" >0.359</td>\n",
-       "      <td id=\"T_489db_row1_col2\" class=\"data row1 col2\" >0.366</td>\n",
-       "      <td id=\"T_489db_row1_col3\" class=\"data row1 col3\" >0.420</td>\n",
-       "      <td id=\"T_489db_row1_col4\" class=\"data row1 col4\" >0.522</td>\n",
-       "      <td id=\"T_489db_row1_col5\" class=\"data row1 col5\" >0.368</td>\n",
-       "      <td id=\"T_489db_row1_col6\" class=\"data row1 col6\" >0.934</td>\n",
-       "      <td id=\"T_489db_row1_col7\" class=\"data row1 col7\" >0.815</td>\n",
-       "      <td id=\"T_489db_row1_col8\" class=\"data row1 col8\" >0.651</td>\n",
-       "      <td id=\"T_489db_row1_col9\" class=\"data row1 col9\" >0.879</td>\n",
-       "      <td id=\"T_489db_row1_col10\" class=\"data row1 col10\" >0.720</td>\n",
-       "      <td id=\"T_489db_row1_col11\" class=\"data row1 col11\" >2.257</td>\n",
-       "      <td id=\"T_489db_row1_col12\" class=\"data row1 col12\" >1.474</td>\n",
-       "      <td id=\"T_489db_row1_col13\" class=\"data row1 col13\" >0.036</td>\n",
-       "      <td id=\"T_489db_row1_col14\" class=\"data row1 col14\" >0.152</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_489db_level0_row2\" class=\"row_heading level0 row2\" >Guided self-play (ours)</th>\n",
-       "      <td id=\"T_489db_row2_col0\" class=\"data row2 col0\" >10</td>\n",
-       "      <td id=\"T_489db_row2_col1\" class=\"data row2 col1\" >0.755</td>\n",
-       "      <td id=\"T_489db_row2_col2\" class=\"data row2 col2\" >0.498</td>\n",
-       "      <td id=\"T_489db_row2_col3\" class=\"data row2 col3\" >0.589</td>\n",
-       "      <td id=\"T_489db_row2_col4\" class=\"data row2 col4\" >0.556</td>\n",
-       "      <td id=\"T_489db_row2_col5\" class=\"data row2 col5\" >0.639</td>\n",
-       "      <td id=\"T_489db_row2_col6\" class=\"data row2 col6\" >0.999</td>\n",
-       "      <td id=\"T_489db_row2_col7\" class=\"data row2 col7\" >0.881</td>\n",
-       "      <td id=\"T_489db_row2_col8\" class=\"data row2 col8\" >0.677</td>\n",
-       "      <td id=\"T_489db_row2_col9\" class=\"data row2 col9\" >0.328</td>\n",
-       "      <td id=\"T_489db_row2_col10\" class=\"data row2 col10\" >0.687</td>\n",
-       "      <td id=\"T_489db_row2_col11\" class=\"data row2 col11\" >52.505</td>\n",
-       "      <td id=\"T_489db_row2_col12\" class=\"data row2 col12\" >52.505</td>\n",
-       "      <td id=\"T_489db_row2_col13\" class=\"data row2 col13\" >0.111</td>\n",
-       "      <td id=\"T_489db_row2_col14\" class=\"data row2 col14\" >0.611</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_489db_level0_row3\" class=\"row_heading level0 row3\" >Random agent</th>\n",
-       "      <td id=\"T_489db_row3_col0\" class=\"data row3 col0\" >10</td>\n",
-       "      <td id=\"T_489db_row3_col1\" class=\"data row3 col1\" >0.002</td>\n",
-       "      <td id=\"T_489db_row3_col2\" class=\"data row3 col2\" >0.116</td>\n",
-       "      <td id=\"T_489db_row3_col3\" class=\"data row3 col3\" >0.014</td>\n",
-       "      <td id=\"T_489db_row3_col4\" class=\"data row3 col4\" >0.034</td>\n",
-       "      <td id=\"T_489db_row3_col5\" class=\"data row3 col5\" >0.000</td>\n",
-       "      <td id=\"T_489db_row3_col6\" class=\"data row3 col6\" >0.000</td>\n",
-       "      <td id=\"T_489db_row3_col7\" class=\"data row3 col7\" >0.735</td>\n",
-       "      <td id=\"T_489db_row3_col8\" class=\"data row3 col8\" >0.148</td>\n",
-       "      <td id=\"T_489db_row3_col9\" class=\"data row3 col9\" >0.191</td>\n",
-       "      <td id=\"T_489db_row3_col10\" class=\"data row3 col10\" >0.144</td>\n",
-       "      <td id=\"T_489db_row3_col11\" class=\"data row3 col11\" >50.739</td>\n",
-       "      <td id=\"T_489db_row3_col12\" class=\"data row3 col12\" >50.706</td>\n",
-       "      <td id=\"T_489db_row3_col13\" class=\"data row3 col13\" >1.000</td>\n",
-       "      <td id=\"T_489db_row3_col14\" class=\"data row3 col14\" >0.613</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f8375366010>"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
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-   ],
-   "source": [
-    "table = display_wosac_table(data_dir=\"../figures_data\")\n",
-    "display(table);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
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-   "source": [
-    "fig, axs = plt.subplots(1, 3, figsize=(12, 3))\n",
-    "\n",
-    "axs[0].hist(df_dist[\"metametric\"], bins=10)\n",
-    "axs[0].set_ylabel(\"Count\")\n",
-    "axs[0].set_xlabel(\"WOSAC composite metric\")\n",
-    "axs[0].grid(True, alpha=.3)\n",
-    "\n",
-    "axs[1].hist(df_dist[\"average_displacement_error\"], bins=10)\n",
-    "axs[1].set_ylabel(\"Count\")\n",
-    "axs[1].set_xlabel(\"Average displacement error\")\n",
-    "axs[1].grid(True, alpha=.3)\n",
-    "\n",
-    "axs[2].hist(df_dist[\"simulated_offroad_rate\"], bins=10)\n",
-    "axs[2].set_ylabel(\"Count\")\n",
-    "axs[2].set_xlabel(\"Offroad rate\")\n",
-    "axs[2].grid(True, alpha=.3)\n",
-    "\n",
-    "#sns.histplot(df_dist, x=\"metametric\", bins=50, kde=False)\n",
-    "\n",
-    "sns.despine()"
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-  {
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diff --git a/examples/eval/notebooks/02_guidance_data_analyis.ipynb b/examples/eval/notebooks/02_guidance_data_analyis.ipynb
index 6018db22e..d64b6a382 100644
--- a/examples/eval/notebooks/02_guidance_data_analyis.ipynb
+++ b/examples/eval/notebooks/02_guidance_data_analyis.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 1,
    "id": "ae958675",
    "metadata": {},
    "outputs": [],
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 2,
    "id": "937797aa",
    "metadata": {},
    "outputs": [],
@@ -91,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 3,
    "id": "db3838a5",
    "metadata": {},
    "outputs": [],
@@ -111,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 4,
    "id": "dce673ef",
    "metadata": {},
    "outputs": [],
@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 5,
    "id": "c216b641",
    "metadata": {},
    "outputs": [
@@ -136,7 +136,7 @@
        "((204, 91, 6), (204, 91, 6), (210, 91, 6))"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -14810,79 +14810,2733 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "ee3b198a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['data/processed/wosac/validation_json_100/5d710aa0c524f747.json']\n"
+     ]
+    }
+   ],
    "source": [
-    "# GUIDANCE_MODE = \"log_replay\"\n",
+    "GUIDANCE_MODE = \"log_replay\"\n",
+    "GUIDANCE_DROPOUT = 0.99\n",
+    "GUIDANCE_DROPOUT_MODE = \"avg\"\n",
     "\n",
-    "# env_config = EnvConfig(\n",
-    "#     reward_type=\"guided_autonomy\",\n",
-    "#     guidance=True,\n",
-    "#     guidance_mode=GUIDANCE_MODE,\n",
-    "#     add_reference_heading=True,\n",
-    "#     add_reference_speed=True,\n",
-    "#     add_reference_pos_xy=True,\n",
-    "#     init_mode=\"wosac_train\",\n",
-    "#     smoothen_trajectory=False,\n",
-    "#     dynamics_model=\"delta_local\",\n",
-    "#     guidance_dropout_prob=0.7,\n",
-    "#     init_steps=0,\n",
-    "# )\n",
-    "# render_config = RenderConfig()\n",
+    "env_config = EnvConfig(\n",
+    "    reward_type=\"guided_autonomy\",\n",
+    "    guidance=True,\n",
+    "    guidance_mode=GUIDANCE_MODE,\n",
+    "    add_reference_heading=True,\n",
+    "    add_reference_speed=True,\n",
+    "    add_reference_pos_xy=True,\n",
+    "    init_mode=\"wosac_train\",\n",
+    "    smoothen_trajectory=False,\n",
+    "    dynamics_model=\"delta_local\",\n",
+    "    guidance_dropout_prob=GUIDANCE_DROPOUT,\n",
+    "    guidance_dropout_mode=GUIDANCE_DROPOUT_MODE,\n",
+    "    init_steps=0,\n",
+    ")\n",
+    "render_config = RenderConfig()\n",
     "\n",
-    "# train_loader = SceneDataLoader(\n",
-    "#     root=DATASET,\n",
-    "#     batch_size=1,\n",
-    "#     dataset_size=100,\n",
-    "#     sample_with_replacement=False,\n",
-    "#     shuffle=False,\n",
-    "#     file_prefix=\"\",\n",
+    "train_loader = SceneDataLoader(\n",
+    "    root=DATASET,\n",
+    "    batch_size=1,\n",
+    "    dataset_size=100,\n",
+    "    sample_with_replacement=False,\n",
+    "    shuffle=True,\n",
+    "    file_prefix=\"\",\n",
     "    \n",
-    "# )\n",
+    ")\n",
     "\n",
-    "# env = GPUDriveTorchEnv(\n",
-    "#     config=env_config,\n",
-    "#     data_loader=train_loader,\n",
-    "#     max_cont_agents=32, \n",
-    "#     device=\"cpu\",\n",
-    "# )\n",
+    "env = GPUDriveTorchEnv(\n",
+    "    config=env_config,\n",
+    "    data_loader=train_loader,\n",
+    "    max_cont_agents=32, \n",
+    "    device=\"cpu\",\n",
+    ")\n",
     "\n",
-    "# print(env.data_batch)\n",
+    "print(env.data_batch)\n",
     "\n",
-    "# obs = env.reset(env.cont_agent_mask)\n",
-    "# expert_actions, _, _, _ = env.get_expert_actions()\n",
+    "obs = env.reset(env.cont_agent_mask)\n",
+    "expert_actions, _, _, _ = env.get_expert_actions()\n",
     "\n",
-    "# # for time_step in range(env.init_steps, 20):\n",
-    "# #     print(f\"Step: {env.step_in_world[0, 0, 0].item()}\")\n",
+    "# for time_step in range(env.init_steps, 20):\n",
+    "#     print(f\"Step: {env.step_in_world[0, 0, 0].item()}\")\n",
     "\n",
-    "# #     # Step the environment\n",
-    "# #     expert_actions, _, _, _ = env.get_expert_actions()\n",
-    "# #     env.step_dynamics(expert_actions[:, :, time_step, :])\n",
+    "#     # Step the environment\n",
+    "#     expert_actions, _, _, _ = env.get_expert_actions()\n",
+    "#     env.step_dynamics(expert_actions[:, :, time_step, :])\n",
     "\n",
-    "# #     obs = env.get_obs(env.cont_agent_mask)\n",
-    "# #     rew = env.get_rewards()\n",
+    "#     obs = env.get_obs(env.cont_agent_mask)\n",
+    "#     rew = env.get_rewards()\n",
     "    \n",
     "\n",
-    "# # # Save for analysis\n",
-    "# # reference_traj = torch.cat([\n",
-    "# #     env.reference_trajectory.pos_xy,\n",
-    "# #     env.reference_trajectory.vel_xy,\n",
-    "# #     env.reference_trajectory.yaw,\n",
-    "# #     env.reference_trajectory.valids,\n",
-    "# # ], dim=-1)\n",
-    "# # reference_traj_np = reference_traj[env.cont_agent_mask].numpy()\n",
+    "# # Save for analysis\n",
+    "# reference_traj = torch.cat([\n",
+    "#     env.reference_trajectory.pos_xy,\n",
+    "#     env.reference_trajectory.vel_xy,\n",
+    "#     env.reference_trajectory.yaw,\n",
+    "#     env.reference_trajectory.valids,\n",
+    "# ], dim=-1)\n",
+    "# reference_traj_np = reference_traj[env.cont_agent_mask].numpy()\n",
     "\n",
-    "# # np.save(f\"reference_{GUIDANCE_MODE}.npy\", reference_traj_np)"
+    "# np.save(f\"reference_{GUIDANCE_MODE}.npy\", reference_traj_np)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "43a62f4b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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+       "   </g>\n",
+       "   <g id=\"patch_5\">\n",
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+       " <defs>\n",
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+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "CENTER_AGENT_IDX = 0\n",
     "\n",
@@ -14898,7 +17552,7 @@
     ")\n",
     "\n",
     "plt.savefig(\n",
-    "    os.path.join(FIGURES_DIR, f\"agent_observation_{CENTER_AGENT_IDX}_{GUIDANCE_MODE}.pdf\"),\n",
+    "    os.path.join(FIGURES_DIR, f\"agent_observation_{CENTER_AGENT_IDX}_{GUIDANCE_MODE}_dropout_{GUIDANCE_DROPOUT}_{GUIDANCE_DROPOUT_MODE}.pdf\"),\n",
     "    dpi=300,\n",
     "    bbox_inches=\"tight\",\n",
     ")"
@@ -14906,10 +17560,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "b4020148",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'plot_agent_guidance_info' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m fig \u001b[38;5;241m=\u001b[39m \u001b[43mplot_agent_guidance_info\u001b[49m(env\u001b[38;5;241m.\u001b[39mguidance_obs, center_agent_idx\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m);\n\u001b[1;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39msavefig(\n\u001b[1;32m      4\u001b[0m     os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(FIGURES_DIR, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124magent_guidance_info_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mCENTER_AGENT_IDX\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mGUIDANCE_MODE\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.pdf\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m      5\u001b[0m     dpi\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m300\u001b[39m,\n\u001b[1;32m      6\u001b[0m     bbox_inches\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m      7\u001b[0m )\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'plot_agent_guidance_info' is not defined"
+     ]
+    }
+   ],
    "source": [
     "fig = plot_agent_guidance_info(env.guidance_obs, center_agent_idx=1);\n",
     "\n",
diff --git a/examples/eval/notebooks/04_guidance_density.ipynb b/examples/eval/notebooks/04_guidance_density.ipynb
new file mode 100644
index 000000000..765370e80
--- /dev/null
+++ b/examples/eval/notebooks/04_guidance_density.ipynb
@@ -0,0 +1,6045 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a8c62c27",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os\n",
+    "import glob\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import importlib\n",
+    "import gpudrive\n",
+    "importlib.reload(gpudrive)\n",
+    "\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import torch\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# Set working directory to the base directory 'gpudrive_madrona'\n",
+    "working_dir = Path.cwd()\n",
+    "while working_dir.name != 'gpudrive':\n",
+    "    working_dir = working_dir.parent\n",
+    "    if working_dir == Path.home():\n",
+    "        raise FileNotFoundError(\"Base directory 'gpudrive_madrona' not found\")\n",
+    "os.chdir(working_dir)\n",
+    "import torch\n",
+    "from PIL import Image\n",
+    "import seaborn as sns\n",
+    "import warnings\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import datetime\n",
+    "\n",
+    "from gpudrive.env.env_torch import GPUDriveTorchEnv\n",
+    "from gpudrive.env.config import EnvConfig, RenderConfig\n",
+    "from gpudrive.env.dataset import SceneDataLoader\n",
+    "from gpudrive.visualize.utils import img_from_fig\n",
+    "\n",
+    "sns.set(\"notebook\", font_scale=1.05, rc={\"figure.figsize\": (5, 5)})\n",
+    "sns.set_style(\"ticks\", rc={\"figure.facecolor\": \"none\", \"axes.facecolor\": \"none\"})\n",
+    "%config InlineBackend.figure_format = 'svg'\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "plt.set_loglevel(\"WARNING\")\n",
+    "mpl.rcParams[\"lines.markersize\"] = 8\n",
+    "\n",
+    "plt.set_loglevel(\"WARNING\")\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "459ad191",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "FILES = [\n",
+    "    \"examples/eval/figures_data/wosac/score_dist_log_replay_density_0.01_2025-05-14-15.csv\",\n",
+    "    \"examples/eval/figures_data/wosac/score_dist_log_replay_density_0.20_2025-05-14-15.csv\",\n",
+    "    \"examples/eval/figures_data/wosac/score_dist_log_replay_density_0.50_2025-05-14-15.csv\",\n",
+    "    \"examples/eval/figures_data/wosac/score_dist_log_replay_density_0.80_2025-05-14-15.csv\",\n",
+    "    \"examples/eval/figures_data/wosac/score_dist_log_replay_density_1.00_2025-05-14-15.csv\",   \n",
+    "]\n",
+    "densities = [0.01, 0.2, 0.5, 0.8, 1.0]\n",
+    "\n",
+    "# Load dataframes from files and merge into a single dataframe\n",
+    "dfs = []\n",
+    "for idx, file in enumerate(FILES):\n",
+    "    df = pd.read_csv(file)\n",
+    "    df[\"guidance_density\"] = densities[idx]\n",
+    "    dfs.append(df)\n",
+    "df = pd.concat(dfs, ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9cdae638",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>scenario_id</th>\n",
+       "      <th>metametric</th>\n",
+       "      <th>average_displacement_error</th>\n",
+       "      <th>linear_speed_likelihood</th>\n",
+       "      <th>linear_acceleration_likelihood</th>\n",
+       "      <th>angular_speed_likelihood</th>\n",
+       "      <th>angular_acceleration_likelihood</th>\n",
+       "      <th>distance_to_nearest_object_likelihood</th>\n",
+       "      <th>collision_indication_likelihood</th>\n",
+       "      <th>time_to_collision_likelihood</th>\n",
+       "      <th>distance_to_road_edge_likelihood</th>\n",
+       "      <th>offroad_indication_likelihood</th>\n",
+       "      <th>min_average_displacement_error</th>\n",
+       "      <th>traffic_light_violation_likelihood</th>\n",
+       "      <th>simulated_collision_rate</th>\n",
+       "      <th>simulated_offroad_rate</th>\n",
+       "      <th>guidance_density</th>\n",
+       "    </tr>\n",
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+       "      <td>0.500000</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
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+       "      <td>0.792170</td>\n",
+       "      <td>1.221277</td>\n",
+       "      <td>0.559010</td>\n",
+       "      <td>0.256983</td>\n",
+       "      <td>0.174697</td>\n",
+       "      <td>0.140181</td>\n",
+       "      <td>0.424051</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.955552</td>\n",
+       "      <td>0.964307</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>1.221277</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>58c46ba6a925e160</td>\n",
+       "      <td>0.394777</td>\n",
+       "      <td>1.136222</td>\n",
+       "      <td>0.469519</td>\n",
+       "      <td>0.329780</td>\n",
+       "      <td>0.287413</td>\n",
+       "      <td>0.392152</td>\n",
+       "      <td>0.430014</td>\n",
+       "      <td>0.372334</td>\n",
+       "      <td>0.581819</td>\n",
+       "      <td>0.838487</td>\n",
+       "      <td>0.138771</td>\n",
+       "      <td>1.136222</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.142857</td>\n",
+       "      <td>0.285714</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>576d4027d09828d4</td>\n",
+       "      <td>0.721167</td>\n",
+       "      <td>2.674277</td>\n",
+       "      <td>0.314409</td>\n",
+       "      <td>0.313406</td>\n",
+       "      <td>0.191264</td>\n",
+       "      <td>0.249466</td>\n",
+       "      <td>0.399080</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.383757</td>\n",
+       "      <td>0.800094</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>2.674277</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        scenario_id  metametric  average_displacement_error  \\\n",
+       "0  576d4027d09828d4    0.481554                    3.065335   \n",
+       "1  2d0dd05d08b63897    0.817140                    0.807226   \n",
+       "2  3af805166fa9ab44    0.792170                    1.221277   \n",
+       "3  58c46ba6a925e160    0.394777                    1.136222   \n",
+       "4  576d4027d09828d4    0.721167                    2.674277   \n",
+       "\n",
+       "   linear_speed_likelihood  linear_acceleration_likelihood  \\\n",
+       "0                 0.313362                        0.322788   \n",
+       "1                 0.826494                        0.652699   \n",
+       "2                 0.559010                        0.256983   \n",
+       "3                 0.469519                        0.329780   \n",
+       "4                 0.314409                        0.313406   \n",
+       "\n",
+       "   angular_speed_likelihood  angular_acceleration_likelihood  \\\n",
+       "0                  0.295049                         0.271437   \n",
+       "1                  0.276226                         0.310619   \n",
+       "2                  0.174697                         0.140181   \n",
+       "3                  0.287413                         0.392152   \n",
+       "4                  0.191264                         0.249466   \n",
+       "\n",
+       "   distance_to_nearest_object_likelihood  collision_indication_likelihood  \\\n",
+       "0                               0.269305                         0.031575   \n",
+       "1                               0.397479                         0.999002   \n",
+       "2                               0.424051                         0.999002   \n",
+       "3                               0.430014                         0.372334   \n",
+       "4                               0.399080                         0.999002   \n",
+       "\n",
+       "   time_to_collision_likelihood  distance_to_road_edge_likelihood  \\\n",
+       "0                      0.485535                          0.766874   \n",
+       "1                      0.988889                          0.514996   \n",
+       "2                      0.955552                          0.964307   \n",
+       "3                      0.581819                          0.838487   \n",
+       "4                      0.383757                          0.800094   \n",
+       "\n",
+       "   offroad_indication_likelihood  min_average_displacement_error  \\\n",
+       "0                       0.999002                        3.065335   \n",
+       "1                       0.999002                        0.807226   \n",
+       "2                       0.999002                        1.221277   \n",
+       "3                       0.138771                        1.136222   \n",
+       "4                       0.999002                        2.674277   \n",
+       "\n",
+       "   traffic_light_violation_likelihood  simulated_collision_rate  \\\n",
+       "0                            0.999002                  0.500000   \n",
+       "1                            0.999002                  0.000000   \n",
+       "2                            0.999002                  0.000000   \n",
+       "3                            0.999002                  0.142857   \n",
+       "4                            0.999002                  0.000000   \n",
+       "\n",
+       "   simulated_offroad_rate  guidance_density  \n",
+       "0                0.000000              0.01  \n",
+       "1                0.500000              0.01  \n",
+       "2                0.500000              0.01  \n",
+       "3                0.285714              0.01  \n",
+       "4                0.000000              0.01  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "ba2b2530",
+   "metadata": {},
+   "outputs": [
+    {
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+   "source": [
+    "fig, axs = plt.subplots(1, 1, figsize=(10, 5))\n",
+    "\n",
+    "sns.barplot(\n",
+    "    data=df,\n",
+    "    x=\"guidance_density\",\n",
+    "    y=\"metametric\",\n",
+    "    order=[1.0, 0.8, 0.5, 0.01],\n",
+    "    errorbar=\"sd\",\n",
+    ")\n",
+    "axs.bar_label(axs.containers[0], fontsize=10)\n",
+    "sns.despine()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "458c0799",
+   "metadata": {},
+   "outputs": [
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+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pal = sns.color_palette(\"light:b_r\", as_cmap=False)\n",
+    "#pal = sns.light_palette(\"seagreen\", reverse=True, as_cmap=False)\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 1, figsize=(10, 5))\n",
+    "\n",
+    "sns.boxplot(\n",
+    "    data=df,\n",
+    "    x=\"guidance_density\",\n",
+    "    y=\"metametric\",\n",
+    "    order=[1.0, 0.8, 0.5, 0.2, 0.01],\n",
+    "    palette=pal,\n",
+    ")\n",
+    "\n",
+    "sns.swarmplot(\n",
+    "    data=df,\n",
+    "    x=\"guidance_density\",\n",
+    "    y=\"metametric\",\n",
+    "    order=[1.0, 0.8, 0.5, 0.2, 0.01],\n",
+    "    size=5, color=\".3\", alpha=0.7,\n",
+    ")\n",
+    "sns.despine()\n",
+    "axs.grid(True, axis='y', alpha=0.5)\n",
+    "fig.savefig(\n",
+    "    \"examples/eval/figures_data/wosac/log_replay_guidance_density.pdf\",\n",
+    "    dpi=300,\n",
+    "    bbox_inches=\"tight\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "fc2c6573",
+   "metadata": {},
+   "outputs": [
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+      "text/plain": [
+       "<Figure size 680x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.catplot(\n",
+    "    data=df, x=\"guidance_density\", y=\"metametric\", capsize=.2, palette=pal, errorbar=\"sd\",\n",
+    "    kind=\"point\", height=4, aspect=1.7, order=[1.0, 0.8, 0.5, 0.2, 0.01],\n",
+    ")\n",
+    "g.despine(left=True)\n",
+    "g.ax.grid(True, axis='y', alpha=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "e439cbc6",
+   "metadata": {},
+   "outputs": [
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+       "L 609.262547 284.4 \n",
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+       "  </g>\n",
+       " </g>\n",
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+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 1, figsize=(10, 5))\n",
+    "\n",
+    "sns.violinplot(\n",
+    "    data=df,\n",
+    "    x=\"guidance_density\",\n",
+    "    y=\"metametric\",\n",
+    "    order=[1.0, 0.8, 0.5, 0.2, 0.01],\n",
+    ")\n",
+    "\n",
+    "sns.despine()\n",
+    "axs.grid(True, alpha=0.5)\n",
+    "# fig.savefig(\n",
+    "#     \"examples/eval/figures_data/wosac/log_replay_guidance_density.pdf\",\n",
+    "#     dpi=300,\n",
+    "#     bbox_inches=\"tight\",\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13eb0fe2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0faa1e7c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "gpudrive",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/eval/rollout_test.py b/examples/eval/rollout_test.py
index ac40a3f97..6a38c64b1 100644
--- a/examples/eval/rollout_test.py
+++ b/examples/eval/rollout_test.py
@@ -28,7 +28,8 @@ def test_rollout(focus_agents=[0, 1], render=False, agent=None):
         off_road_weight=-0.0,
         guidance_heading_weight=0.005,
         guidance_speed_weight=0.0005,
-        
+        guidance_dropout_prob=0.99,
+        guidance_dropout_mode="avg",  # Options: "max", "avg"
     )
 
     # Create data loader
@@ -162,8 +163,8 @@ def test_rollout(focus_agents=[0, 1], render=False, agent=None):
     CPT_PATH = (
         "checkpoints/model_guidance_logs__S_1__05_12_18_44_58_857_000100.pt"
     )
-    agent = load_agent(path_to_cpt=CPT_PATH).to("cuda")
-
+    #agent = load_agent(path_to_cpt=CPT_PATH).to("cuda")
+    agent = None
     
     
     test_rollout(focus_agents=[0, 1, 2, 3, 4], render=True, agent=agent)
diff --git a/examples/eval/run_wosac_eval.py b/examples/eval/run_wosac_eval.py
index 3ab2928a5..17b999ef4 100644
--- a/examples/eval/run_wosac_eval.py
+++ b/examples/eval/run_wosac_eval.py
@@ -70,6 +70,7 @@ def rollout(
     save_videos: bool = True,
     video_dir: str = "videos",
     video_format: str = "gif",
+    guidance_mode: str = "vbd_online",
 ):
     """Rollout agent in the environment and return the scenario rollouts."""
     # Storage
@@ -79,22 +80,27 @@ def rollout(
 
     start_env_rollout = perf_counter()
 
-    _ = env.reset()
-
     # Zero out actions for parked vehicles
     info = Info.from_tensor(
         env.sim.info_tensor(),
         backend=env.backend,
         device=env.device,
     )
-    control_mask_all = env.cont_agent_mask.clone()
+
     zero_action_mask = (info.off_road == 1) | (
         info.collided_with_vehicle == 1
     ) & (info.type == int(madrona_gpudrive.EntityType.Vehicle))
-    control_mask = control_mask_all & ~zero_action_mask
+    control_mask = env.cont_agent_mask.clone()
 
     next_obs = env.reset(mask=control_mask)
 
+    # Guidance logging
+    num_guidance_points = env.valid_guidance_points
+    guidance_densities = num_guidance_points / env.reference_traj_len
+    print(
+        f"Avg guidance points per agent: {num_guidance_points.cpu().numpy().mean():.2f} which is {guidance_densities.mean().item()*100:.2f} % of the trajectory length (mode = {env.config.guidance_dropout_mode}) \n"
+    )
+
     # Get scenario ids
     scenario_ids_dict = env.get_scenario_ids()
     scenario_ids = list(scenario_ids_dict.values())
@@ -105,6 +111,7 @@ def rollout(
     heading_list = []
     done_list = [env.get_dones()]
 
+    # Get initial states
     pos_x, pos_y, pos_z, heading, _ = get_state(env)
 
     for time_step in range(env.episode_len - init_steps):
@@ -117,6 +124,17 @@ def rollout(
         )
         action_template[control_mask] = action.to(device)
 
+        # Find the integer key for the "do nothing" action (zero steering, zero acceleration)
+        # Check using env.action_key_to_values[DO_NOTHING_ACTION_INT]
+        DO_NOTHING_ACTION_INT = [
+            key
+            for key, value in env.action_key_to_values.items()
+            if abs(value[0]) == 0.0
+            and abs(value[1]) == 0.0
+            and abs(value[2]) == 0.0
+        ][0]
+        action_template[zero_action_mask] = DO_NOTHING_ACTION_INT
+
         # Step
         env.step_dynamics(action_template)
 
@@ -165,7 +183,7 @@ def rollout(
                 and len(simulator_state_frames[idx]) > 0
             ):
                 mediapy.write_video(
-                    f"{video_dir}/sim_state_env_{idx}_{scenario_id}.{video_format}",
+                    f"{video_dir}/{guidance_mode}_sim_state_env_{idx}_{scenario_id}.{video_format}",
                     np.array(simulator_state_frames[idx]),
                     fps=8,
                     codec=video_format,
@@ -174,7 +192,7 @@ def rollout(
         if render_agent_pov and len(agent_observation_frames[0]) > 0:
             scenario_id = scenario_ids_dict[0]
             mediapy.write_video(
-                f"{video_dir}/agent_0_{scenario_id}.{video_format}",
+                f"{video_dir}/{guidance_mode}_agent_0_{scenario_id}.{video_format}",
                 np.array(agent_observation_frames[0]),
                 fps=8,
                 codec=video_format,
@@ -246,25 +264,28 @@ def load_config(config_path):
 if __name__ == "__main__":
 
     # Settings
-    MAX_AGENTS = 32  # TODO: Set to 128 for real eval
-    NUM_ENVS = 20
+    MAX_AGENTS = (
+        madrona_gpudrive.kMaxAgentCount
+    )  # TODO: Set to 128 for real eval
+    NUM_ENVS = 100
     DEVICE = "cuda"  # where to run the env rollouts
     NUM_ROLLOUTS_PER_BATCH = 1
     NUM_DATA_BATCHES = 1
     INIT_STEPS = 10
     DATASET_SIZE = 100
-    RENDER = True
+    RENDER = False
     LOG_DIR = "examples/eval/figures_data/wosac/"
     GUIDANCE_MODE = (
         "log_replay"  # Options: "vbd_amortized", "vbd_online", "log_replay"
     )
+    GUIDANCE_DROPOUT_MODE = "avg"  # Options: "max", "avg", "remove_all"
+    GUIDANCE_DROPOUT_PROB = 0.0
+    SMOOTHEN_TRAJECTORY = True
 
-    DATA_JSON = "data/processed/wosac/validation_json_100"
-    DATA_TFRECORD = "data/processed/wosac/validation_tfrecord_100"
+    DATA_JSON = "data/processed/wosac/validation/json"
+    DATA_TFRECORD = "data/processed/wosac/validation/tfrecord"
 
-    CPT_PATH = (
-        "checkpoints/model_guidance_logs__S_100__05_12_23_14_58_678_001000.pt"
-    )
+    CPT_PATH = "checkpoints/model_guidance_logs__R_10000__05_14_16_54_46_975_000300.pt"
 
     # Create data loader
     val_loader = SceneDataLoader(
@@ -274,6 +295,7 @@ def load_config(config_path):
         sample_with_replacement=True,
         shuffle=True,
         file_prefix="",
+        seed=10,
     )
 
     # Load agent
@@ -297,11 +319,9 @@ def load_config(config_path):
         norm_obs=config.norm_obs,
         add_previous_action=config.add_previous_action,
         guidance=config.guidance,
-        guidance_dropout_prob=config.guidance_dropout_prob,
         add_reference_pos_xy=config.add_reference_pos_xy,
         add_reference_speed=config.add_reference_speed,
         add_reference_heading=config.add_reference_heading,
-        smoothen_trajectory=config.smoothen_trajectory,
         dynamics_model=config.dynamics_model,
         collision_behavior=config.collision_behavior,
         goal_behavior=config.goal_behavior,
@@ -333,6 +353,9 @@ def load_config(config_path):
         init_mode="wosac_eval",
         init_steps=INIT_STEPS,
         guidance_mode=GUIDANCE_MODE,
+        guidance_dropout_prob=GUIDANCE_DROPOUT_PROB,
+        guidance_dropout_mode=GUIDANCE_DROPOUT_MODE,
+        smoothen_trajectory=SMOOTHEN_TRAJECTORY,
     )
 
     # Make environment
@@ -346,6 +369,8 @@ def load_config(config_path):
     wosac_metrics = WOSACMetrics(
         save_table_with_baselines=True,
         log_dir=LOG_DIR,
+        guidance_mode=GUIDANCE_MODE,
+        guidance_density=1.0 - GUIDANCE_DROPOUT_PROB,
     )
 
     for _ in tqdm(range(NUM_DATA_BATCHES)):
@@ -361,6 +386,7 @@ def load_config(config_path):
                 render_simulator_states=RENDER,
                 render_agent_pov=RENDER,
                 save_videos=RENDER,
+                guidance_mode=GUIDANCE_MODE,
             )
             tf_record_paths = [
                 os.path.join(DATA_TFRECORD, f"{scenario_id}.tfrecords")
diff --git a/examples/eval/wosac_eval_origin.py b/examples/eval/wosac_eval_origin.py
index fcf4d50c8..88398f467 100644
--- a/examples/eval/wosac_eval_origin.py
+++ b/examples/eval/wosac_eval_origin.py
@@ -35,6 +35,8 @@ def __init__(
         baselines_df: Optional[pd.DataFrame] = None,
         save_table_with_baselines: bool = True,
         log_dir: Optional[str] = None,
+        guidance_mode: Optional[str] = "",
+        guidance_density: Optional[float] = 1.0,
     ) -> None:
         super().__init__()
         self.is_mp_init = False
@@ -48,6 +50,8 @@ def __init__(
             self.challenge_type = challenge_type
 
         self.wosac_config = load_metrics_config(self.challenge_type)
+        self.guidance_mode = guidance_mode
+        self.guidance_density = guidance_density
 
         # Initialize baseline df if not provided
         if baselines_df is None:
@@ -55,7 +59,7 @@ def __init__(
         else:
             self.baselines_df = baselines_df
 
-        self.timestamp = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M")
+        self.timestamp = datetime.datetime.now().strftime("%Y-%m-%d-%H")
 
         self.field_names = [
             "metametric",
@@ -84,14 +88,20 @@ def __init__(
         if log_dir is not None:
             # Get data and time for the current run as filename
             log_dir = Path(log_dir)
-            self.log_file_path = log_dir / f"score_distribution_{self.timestamp}.csv"
-            self.log_aggregate_table_path = log_dir / f"wosac_table_{self.timestamp}.csv"
+            self.log_file_path = (
+                log_dir
+                / f"score_dist_{self.guidance_mode}_density_{self.guidance_density:.2f}_{self.timestamp}.csv"
+            )
+            self.log_aggregate_table_path = (
+                log_dir
+                / f"wosac_agg_{self.guidance_mode}_density_{self.guidance_density:.2f}_{self.timestamp}.csv"
+            )
             log_dir.mkdir(parents=True, exist_ok=True)
-            self.log_file = csv.writer(open(self.log_file_path, 'w'))
+            self.log_file = csv.writer(open(self.log_file_path, "w"))
             # Add header to the log file
             all_rows = self.field_names.copy()
             all_rows.insert(0, "scenario_id")
-            self.log_file.writerow(all_rows) 
+            self.log_file.writerow(all_rows)
 
         else:
             self.log_file = None
@@ -230,18 +240,16 @@ def update(
         ):
             try:
                 scenario_result = self._compute_scenario_metrics(
-                        self.wosac_config,
-                        _scenario,
-                        _scenario_rollout,
-                        self.ego_only,
-                        # scenario_rollouts_mask=_scenario_mask
-                    )
-                pool_scenario_metrics.append(
-                    scenario_result
+                    self.wosac_config,
+                    _scenario,
+                    _scenario_rollout,
+                    self.ego_only,
+                    # scenario_rollouts_mask=_scenario_mask
                 )
+                pool_scenario_metrics.append(scenario_result)
 
                 if self.log_file is not None:
-                    # Log the scenario metrics to the file  
+                    # Log the scenario metrics to the file
                     result_to_log = [
                         _scenario_rollout.scenario_id,
                         scenario_result.metametric,
@@ -258,7 +266,7 @@ def update(
                         scenario_result.min_average_displacement_error,
                         scenario_result.traffic_light_violation_likelihood,
                         scenario_result.simulated_collision_rate,
-                        scenario_result.simulated_offroad_rate, 
+                        scenario_result.simulated_offroad_rate,
                     ]
                     self.log_file.writerow(result_to_log)
             except Exception as e:
@@ -339,7 +347,9 @@ def compute(self) -> Dict[str, Tensor]:
         if self.save_table_with_baselines:
             self._add_current_method_to_baselines(metrics_dict, final_metrics)
 
-            self.baselines_df.to_csv(self.log_aggregate_table_path, index=False)
+            self.baselines_df.to_csv(
+                self.log_aggregate_table_path, index=False
+            )
             print(f"Saved df to {self.log_aggregate_table_path}")
 
         return out_dict
diff --git a/gpudrive/env/config.py b/gpudrive/env/config.py
index e549a9013..2c4f9733d 100755
--- a/gpudrive/env/config.py
+++ b/gpudrive/env/config.py
@@ -43,6 +43,7 @@ class EnvConfig:
     smoothen_trajectory: bool = True  # Filters out the trajectory
     guidance_pos_xy_radius: float = 1.0  # Tightness of the positions guidance
     guidance_dropout_prob: float = 0.0  # Probability of dropping the points
+    guidance_dropout_mode: str = "max"  # Options: "max", "avg", "remove_all"
 
     # Maximum number of controlled agents in the scene
     max_controlled_agents: int = madrona_gpudrive.kMaxAgentCount
@@ -126,7 +127,7 @@ class EnvConfig:
         0.005  # Importance of matching suggested speeds
     )
     guidance_heading_weight: float = (
-        0.005 # Importance of matching suggested headings
+        0.005  # Importance of matching suggested headings
     )
     smoothness_weight: float = 0.0
 
diff --git a/gpudrive/env/env_torch.py b/gpudrive/env/env_torch.py
index 9db9717f6..c4c41bf2c 100755
--- a/gpudrive/env/env_torch.py
+++ b/gpudrive/env/env_torch.py
@@ -97,6 +97,8 @@ def __init__(
 
         self.setup_guidance()
 
+        self.guidance_dropout_mask = self.create_guidance_dropout_mask()
+
         if self.config.reward_type == "reward_conditioned":
             # Use default condition_mode from config or fall back to "random"
             condition_mode = getattr(self.config, "condition_mode", "random")
@@ -302,9 +304,7 @@ def construct_context(self, init_steps=10):
                 - 'agents_id': Unique identifiers for each agent
                 - 'anchors': Reference points
         """
-        means_xy = (
-            self.sim.world_means_tensor().to_torch()[:, :2].to("cpu")
-        )
+        means_xy = self.sim.world_means_tensor().to_torch()[:, :2].to("cpu")
 
         # Get the logged trajectory and restore the mean
         log_trajectory = LogTrajectory.from_tensor(
@@ -355,7 +355,7 @@ def construct_context(self, init_steps=10):
             metadata=metadata,
         )
 
-        if (self.device != "cpu"):
+        if self.device != "cpu":
             # Move tensors to the specified device
             for key in context_dict:
                 if isinstance(context_dict[key], torch.Tensor):
@@ -884,53 +884,115 @@ def get_rewards(
 
     def create_guidance_dropout_mask(self):
         """
-        Create guidance dropout mask where dropout_prob represents
-        the maximum dropout probability, with varying rates per trajectory.
-        """
-        max_dropout_prob = self.config.guidance_dropout_prob
-        num_controlled = self.cont_agent_mask.sum().item()
+        Create guidance dropout mask based on the specified dropout mode.
 
-        # 1 if we want to keep the point, 0 if we want to drop it
-        guidance_dropout_mask = torch.ones(
-            (num_controlled, self.reference_traj_len),
-            device=self.device,
-            dtype=torch.bool,
-        )
+        Args:
+            mask: Optional pre-existing mask to modify
+            dropout_mode: Either "max" (default) or "avg"
+                - "max": dropout_prob represents the maximum dropout probability
+                - "avg": dropout_prob represents the average dropout probability
 
-        is_valid = (
-            self.reference_trajectory.valids[self.cont_agent_mask]
-            .squeeze(-1)
-            .bool()
-        )
+        Returns:
+            A boolean mask where True indicates keeping the point, False indicates dropping it
+        """
+        dropout_prob = self.config.guidance_dropout_prob
+        num_controlled = self.cont_agent_mask.sum().item()
+        self.dropout_mode = self.config.guidance_dropout_mode
 
-        # Generate random dropout rates for each agent between 0 and max_dropout_prob
-        agent_dropout_probs = (
-            torch.rand(num_controlled, device=self.device) * max_dropout_prob
-        )
+        if self.dropout_mode == "remove_all":
+            # If the mode is "remove_all", we want to drop all points
+            # so the agents receive no guidance
+            return torch.zeros(
+                (num_controlled, self.reference_traj_len),
+                device=self.device,
+                dtype=torch.bool,
+            )
+        else:
+            # 1 if we want to keep the point, 0 if we want to drop it
+            guidance_dropout_mask = torch.ones(
+                (num_controlled, self.reference_traj_len),
+                device=self.device,
+                dtype=torch.bool,
+            )
 
-        for agent_idx in range(num_controlled):
-            agent_valid_mask = is_valid[agent_idx]
-            agent_valid_indices = torch.where(agent_valid_mask)[0]
+            is_valid = (
+                self.reference_trajectory.valids[self.cont_agent_mask]
+                .squeeze(-1)
+                .bool()
+            )
 
-            # Get agent-specific dropout probability
-            agent_dropout_prob = agent_dropout_probs[agent_idx]
+            if dropout_prob > 0.0:
 
-            if (
-                len(agent_valid_indices) > 2
-            ):  # Only apply dropout if we have more than 2 points
-                # Keep first and last points, apply dropout to middle points
-                middle_indices = agent_valid_indices[1:-1]
-
-                # Generate random dropout mask for middle points
-                dropout = (
-                    torch.rand(len(middle_indices), device=self.device)
-                    < agent_dropout_prob
-                )
+                if self.dropout_mode == "max":
+                    # Generate random dropout rates for each agent between 0 and dropout_prob
+                    agent_dropout_probs = (
+                        torch.rand(num_controlled, device=self.device)
+                        * dropout_prob
+                    )
+                elif self.dropout_mode == "avg":
+                    # Use the same dropout probability for all agents (equal to dropout_prob)
+                    agent_dropout_probs = torch.full(
+                        (num_controlled,), dropout_prob, device=self.device
+                    )
+                else:
+                    raise ValueError(
+                        f"Unsupported dropout mode: {self.config.dropout_mode}"
+                    )
 
-                # Apply dropout to middle points (set to False for points to drop)
-                guidance_dropout_mask[agent_idx, middle_indices] = ~dropout
+                for agent_idx in range(num_controlled):
+                    agent_valid_mask = is_valid[agent_idx]
+                    agent_valid_indices = torch.where(agent_valid_mask)[0]
+
+                    # Get agent-specific dropout probability
+                    agent_dropout_prob = agent_dropout_probs[agent_idx]
+
+                    if (
+                        len(agent_valid_indices) > 2
+                    ):  # Only apply dropout if we have more than 2 points
+                        if self.dropout_mode == "max":
+                            # Keep first and last points, apply dropout to middle points
+                            middle_indices = agent_valid_indices[1:-1]
+
+                            # Generate random dropout mask for middle points
+                            dropout = (
+                                torch.rand(
+                                    len(middle_indices), device=self.device
+                                )
+                                < agent_dropout_prob
+                            )
+
+                            # Apply dropout to middle points (set to False for points to drop)
+                            guidance_dropout_mask[
+                                agent_idx, middle_indices
+                            ] = ~dropout
+
+                        elif self.dropout_mode == "avg":
+                            # Calculate how many points to drop to achieve the desired average dropout rate
+                            middle_indices = agent_valid_indices[
+                                :-1
+                            ]  # All valid points except the last one
+                            num_middle = len(middle_indices)
+
+                            # Number of points to drop to achieve the desired average
+                            num_to_drop = int(
+                                np.ceil(
+                                    (agent_dropout_prob.item() * num_middle)
+                                )
+                            )
+
+                            if num_to_drop > 0 and num_middle > 0:
+                                # Randomly select points to drop
+                                drop_indices = middle_indices[
+                                    torch.randperm(num_middle)[:num_to_drop]
+                                ]
+                                guidance_dropout_mask[
+                                    agent_idx, drop_indices
+                                ] = False
+
+                return guidance_dropout_mask
 
-        return guidance_dropout_mask
+            else:
+                return guidance_dropout_mask
 
     def guidance_points_within_reach(self):
         # Get actual agent positions
@@ -1358,6 +1420,7 @@ def _get_ego_state(self, mask=None) -> torch.Tensor:
 
         if mask is None:
 
+            # TODO(dc): Make this one exactly the same as for the masked case
             # New: Give agent sense of progress / history
             # 1. Overall trajectory progress (percentage of valid points hit)
             total_valid = self.reference_trajectory.valids.clone().sum(
@@ -1396,25 +1459,23 @@ def _get_ego_state(self, mask=None) -> torch.Tensor:
                 return torch.cat(base_fields, dim=-1)
         else:
 
-            # New: Give agent sense of progress / history
-            # 1. Overall trajectory progress (percentage of valid points hit)
-            total_valid = (
-                self.reference_trajectory.valids[mask]
-                .clone()
-                .sum(dim=1)
-                .squeeze(-1)
-            )
-            valid_hits_so_far = self.guidance_points_hit.clone()[mask].sum(
-                dim=-1
-            )
+            # New: Give agent sense of progress
+            # 1. Overall trajectory progress
+            # Only increment route progress for visible points hit (those that
+            # are not dropped out)
+            total_valid = self.guidance_dropout_mask.sum(axis=1)
+            valid_hits_so_far = self.guidance_points_hit.clone()[mask]
+            visible_hits_so_far = (
+                valid_hits_so_far * self.guidance_dropout_mask
+            ).sum(dim=-1)
 
             # Handle the case where there are zero valid points
             # If total_valid is 0, we consider the route 100% complete (progress = 1.0)
             self.route_progress = torch.where(
                 total_valid >= 1,
-                valid_hits_so_far / (total_valid + 1e-5),  # Normal case
+                visible_hits_so_far / (total_valid + 1e-5),  # Normal case
                 torch.ones_like(
-                    valid_hits_so_far
+                    visible_hits_so_far
                 ),  # When agent has no valid points
             )
 
diff --git a/gpudrive/utils/generate_sbatch.py b/gpudrive/utils/generate_sbatch.py
index 07db7ea8e..c15714fc2 100644
--- a/gpudrive/utils/generate_sbatch.py
+++ b/gpudrive/utils/generate_sbatch.py
@@ -265,9 +265,9 @@ def save_script(filename, file_path, fields, params, param_order=None, exclude_n
 
 if __name__ == "__main__":
 
-    group = "mem_optimization_2" 
+    group = "10k_dropout_v3" 
     fields = {
-        "time_h": 1,  # Max time per job (job will finish if run is done before)
+        "time_h": 47,  # Max time per job (job will finish if run is done before)
         "num_gpus": 1,  # GPUs per job
         "max_sim_jobs": 30,  # Max jobs at the same time
         "memory": 80,
@@ -277,19 +277,20 @@ def save_script(filename, file_path, fields, params, param_order=None, exclude_n
 
     hyperparams = {
         "group": [group],  # Group name
-        "num_worlds": [500, 700],
+        "num_worlds": [700],
         "data_dir": ["/scratch/kj2676/gpudrive/data/processed/training"],
-        "resample_scenes": [0, 1],
-        "k_unique_scenes": [500],
+        "resample_scenes": [1],
+        "k_unique_scenes": [700],
         "resample_interval": [5_000_000],
-        "cpu_offload": [0, 1],
         "batch_size": [1],
-        # "resample_dataset_size": [10_000],
-        # "total_timesteps": [3_000_000_000],
-        "batch_size": [1048576, 2097152],
-        # "minibatch_size": [16384],
-        # "ent_coef": [0.01, 0.003],
-        # "vf_coef": [0.5],
+        "guidance_dropout_prob": [0.0, 0.95],
+        "off_road_weight": [-0.1, -0.01, 0.0],
+        "resample_dataset_size": [10_000],
+        "total_timesteps": [5_000_000_000],
+        "batch_size": [1048576],
+        "minibatch_size": [16384],
+        "ent_coef": [0.01],
+        "vf_coef": [0.5],
         "update_epochs": [1],
         "render": [0],
     }

From f0ad20f0ea66312fc5c30673c45b419bb8116131 Mon Sep 17 00:00:00 2001
From: Daphne Cornelisse <33460159+daphne-cornelisse@users.noreply.github.com>
Date: Thu, 15 May 2025 15:06:39 -0400
Subject: [PATCH 18/38] More updates (#450)

* Fix: ensure vbd_online can be used on cuda and cpu.

* Small updates

* WOSAC defaults

* Dynamics model fix

* Minor

* Update sbatch

* Code to make latex wosac tables

* pkl file

* Add smoothness

* Add new dropout mode

* WOSAC eval improvements

* Add figures

* Increment progress based on visible route points only.

* Files

* Some small updates

* Minor

* WOSAC eval
---
 baselines/ppo/config/ppo_population.yaml      |    6 +-
 .../eval/notebooks/04_guidance_density.ipynb  | 3582 +++++++++++++++++
 examples/eval/run_wosac_eval.py               |   19 +-
 gpudrive/env/env_torch.py                     |    2 +
 src/consts.hpp                                |    2 +-
 5 files changed, 3598 insertions(+), 13 deletions(-)

diff --git a/baselines/ppo/config/ppo_population.yaml b/baselines/ppo/config/ppo_population.yaml
index 06378f9a5..2c3d29513 100644
--- a/baselines/ppo/config/ppo_population.yaml
+++ b/baselines/ppo/config/ppo_population.yaml
@@ -8,8 +8,8 @@ model_cpt: null
 
 environment: # Overrides default environment configs (see pygpudrive/env/config.py)
   name: "gpudrive"
-  num_worlds: 100 # Number of parallel environments
-  k_unique_scenes: 100 # Number of unique scenes to sample from
+  num_worlds: 10 # Number of parallel environments
+  k_unique_scenes: 10 # Number of unique scenes to sample from
   max_controlled_agents: 64 # Maximum number of agents controlled by the model. Make sure this aligns with the variable kMaxAgentCount in src/consts.hpp
   ego_state: true
   road_map_obs: true
@@ -46,7 +46,7 @@ environment: # Overrides default environment configs (see pygpudrive/env/config.
 wandb:
   entity: ""
   project: "kshotagents"
-  group: "separate_actor_critic"
+  group: "debug_mini"
   mode: "online" # Options: online, offline, disabled
   tags: ["ppo", "ff"]
 
diff --git a/examples/eval/notebooks/04_guidance_density.ipynb b/examples/eval/notebooks/04_guidance_density.ipynb
index 765370e80..c5a3cdfc6 100644
--- a/examples/eval/notebooks/04_guidance_density.ipynb
+++ b/examples/eval/notebooks/04_guidance_density.ipynb
@@ -2,7 +2,11 @@
  "cells": [
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 57,
+=======
    "execution_count": 2,
+>>>>>>> dev
    "id": "a8c62c27",
    "metadata": {},
    "outputs": [],
@@ -54,12 +58,26 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 60,
+=======
    "execution_count": 12,
+>>>>>>> dev
    "id": "459ad191",
    "metadata": {},
    "outputs": [],
    "source": [
     "FILES = [\n",
+<<<<<<< HEAD
+    "    \"examples/eval/figures_data/wosac/score_dist_log_replay_density_0.00_2025-05-14-21.csv\",\n",
+    "    \"examples/eval/figures_data/wosac/score_dist_log_replay_density_0.01_2025-05-14-21.csv\",\n",
+    "    \"examples/eval/figures_data/wosac/score_dist_log_replay_density_0.25_2025-05-14-21.csv\",\n",
+    "    \"examples/eval/figures_data/wosac/score_dist_log_replay_density_0.50_2025-05-14-21.csv\",\n",
+    "    \"examples/eval/figures_data/wosac/score_dist_log_replay_density_0.75_2025-05-14-21.csv\",\n",
+    "    \"examples/eval/figures_data/wosac/score_dist_log_replay_density_1.00_2025-05-14-20.csv\",   \n",
+    "]\n",
+    "densities = [0.0, 0.01, 0.25, 0.5, 0.75, 1.0]\n",
+=======
     "    \"examples/eval/figures_data/wosac/score_dist_log_replay_density_0.01_2025-05-14-15.csv\",\n",
     "    \"examples/eval/figures_data/wosac/score_dist_log_replay_density_0.20_2025-05-14-15.csv\",\n",
     "    \"examples/eval/figures_data/wosac/score_dist_log_replay_density_0.50_2025-05-14-15.csv\",\n",
@@ -67,6 +85,7 @@
     "    \"examples/eval/figures_data/wosac/score_dist_log_replay_density_1.00_2025-05-14-15.csv\",   \n",
     "]\n",
     "densities = [0.01, 0.2, 0.5, 0.8, 1.0]\n",
+>>>>>>> dev
     "\n",
     "# Load dataframes from files and merge into a single dataframe\n",
     "dfs = []\n",
@@ -74,12 +93,22 @@
     "    df = pd.read_csv(file)\n",
     "    df[\"guidance_density\"] = densities[idx]\n",
     "    dfs.append(df)\n",
+<<<<<<< HEAD
+    "df = pd.concat(dfs, ignore_index=True)\n",
+    "\n",
+    "ORDER = [1.0, 0.75, 0.5, 0.25, 0.01, 0.0]"
+=======
     "df = pd.concat(dfs, ignore_index=True)"
+>>>>>>> dev
    ]
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 61,
+=======
    "execution_count": 13,
+>>>>>>> dev
    "id": "9cdae638",
    "metadata": {},
    "outputs": [
@@ -126,6 +155,105 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
+<<<<<<< HEAD
+       "      <td>18aa438fe00f01ca</td>\n",
+       "      <td>0.783960</td>\n",
+       "      <td>2.395037</td>\n",
+       "      <td>0.314201</td>\n",
+       "      <td>0.374656</td>\n",
+       "      <td>0.545279</td>\n",
+       "      <td>0.526871</td>\n",
+       "      <td>0.129361</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.954806</td>\n",
+       "      <td>0.760846</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>2.395037</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13d80264125ba0b4</td>\n",
+       "      <td>0.751225</td>\n",
+       "      <td>1.911907</td>\n",
+       "      <td>0.415180</td>\n",
+       "      <td>0.371000</td>\n",
+       "      <td>0.368349</td>\n",
+       "      <td>0.449421</td>\n",
+       "      <td>0.372491</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.401211</td>\n",
+       "      <td>0.884115</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>1.911907</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>159a8e178d2ed73a</td>\n",
+       "      <td>0.867504</td>\n",
+       "      <td>1.472178</td>\n",
+       "      <td>0.427534</td>\n",
+       "      <td>0.637751</td>\n",
+       "      <td>0.796629</td>\n",
+       "      <td>0.649286</td>\n",
+       "      <td>0.804871</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.723108</td>\n",
+       "      <td>0.793909</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>1.472178</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>194921d60eb9f3ca</td>\n",
+       "      <td>0.765869</td>\n",
+       "      <td>1.569921</td>\n",
+       "      <td>0.542538</td>\n",
+       "      <td>0.655502</td>\n",
+       "      <td>0.256704</td>\n",
+       "      <td>0.337313</td>\n",
+       "      <td>0.118410</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.988889</td>\n",
+       "      <td>0.321709</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>1.569921</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>18aa438fe00f01ca</td>\n",
+       "      <td>0.777547</td>\n",
+       "      <td>2.507769</td>\n",
+       "      <td>0.270521</td>\n",
+       "      <td>0.333834</td>\n",
+       "      <td>0.522520</td>\n",
+       "      <td>0.461351</td>\n",
+       "      <td>0.117017</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.988889</td>\n",
+       "      <td>0.761873</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>2.507769</td>\n",
+       "      <td>0.999002</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+=======
        "      <td>576d4027d09828d4</td>\n",
        "      <td>0.481554</td>\n",
        "      <td>3.065335</td>\n",
@@ -223,6 +351,7 @@
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.01</td>\n",
+>>>>>>> dev
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -230,6 +359,65 @@
       ],
       "text/plain": [
        "        scenario_id  metametric  average_displacement_error  \\\n",
+<<<<<<< HEAD
+       "0  18aa438fe00f01ca    0.783960                    2.395037   \n",
+       "1  13d80264125ba0b4    0.751225                    1.911907   \n",
+       "2  159a8e178d2ed73a    0.867504                    1.472178   \n",
+       "3  194921d60eb9f3ca    0.765869                    1.569921   \n",
+       "4  18aa438fe00f01ca    0.777547                    2.507769   \n",
+       "\n",
+       "   linear_speed_likelihood  linear_acceleration_likelihood  \\\n",
+       "0                 0.314201                        0.374656   \n",
+       "1                 0.415180                        0.371000   \n",
+       "2                 0.427534                        0.637751   \n",
+       "3                 0.542538                        0.655502   \n",
+       "4                 0.270521                        0.333834   \n",
+       "\n",
+       "   angular_speed_likelihood  angular_acceleration_likelihood  \\\n",
+       "0                  0.545279                         0.526871   \n",
+       "1                  0.368349                         0.449421   \n",
+       "2                  0.796629                         0.649286   \n",
+       "3                  0.256704                         0.337313   \n",
+       "4                  0.522520                         0.461351   \n",
+       "\n",
+       "   distance_to_nearest_object_likelihood  collision_indication_likelihood  \\\n",
+       "0                               0.129361                         0.999002   \n",
+       "1                               0.372491                         0.999002   \n",
+       "2                               0.804871                         0.999002   \n",
+       "3                               0.118410                         0.999002   \n",
+       "4                               0.117017                         0.999002   \n",
+       "\n",
+       "   time_to_collision_likelihood  distance_to_road_edge_likelihood  \\\n",
+       "0                      0.954806                          0.760846   \n",
+       "1                      0.401211                          0.884115   \n",
+       "2                      0.723108                          0.793909   \n",
+       "3                      0.988889                          0.321709   \n",
+       "4                      0.988889                          0.761873   \n",
+       "\n",
+       "   offroad_indication_likelihood  min_average_displacement_error  \\\n",
+       "0                       0.999002                        2.395037   \n",
+       "1                       0.999002                        1.911907   \n",
+       "2                       0.999002                        1.472178   \n",
+       "3                       0.999002                        1.569921   \n",
+       "4                       0.999002                        2.507769   \n",
+       "\n",
+       "   traffic_light_violation_likelihood  simulated_collision_rate  \\\n",
+       "0                            0.999002                       0.0   \n",
+       "1                            0.999002                       0.0   \n",
+       "2                            0.999002                       0.0   \n",
+       "3                            0.999002                       0.0   \n",
+       "4                            0.999002                       0.0   \n",
+       "\n",
+       "   simulated_offroad_rate  guidance_density  \n",
+       "0                   0.000               0.0  \n",
+       "1                   0.125               0.0  \n",
+       "2                   0.000               0.0  \n",
+       "3                   0.000               0.0  \n",
+       "4                   0.000               0.0  "
+      ]
+     },
+     "execution_count": 61,
+=======
        "0  576d4027d09828d4    0.481554                    3.065335   \n",
        "1  2d0dd05d08b63897    0.817140                    0.807226   \n",
        "2  3af805166fa9ab44    0.792170                    1.221277   \n",
@@ -287,6 +475,7 @@
       ]
      },
      "execution_count": 13,
+>>>>>>> dev
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -297,7 +486,11 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 69,
+=======
    "execution_count": 52,
+>>>>>>> dev
    "id": "ba2b2530",
    "metadata": {},
    "outputs": [
@@ -312,7 +505,11 @@
        "  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n",
        "   <cc:Work>\n",
        "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
+<<<<<<< HEAD
+       "    <dc:date>2025-05-14T22:16:21.320193</dc:date>\n",
+=======
        "    <dc:date>2025-05-14T17:27:14.234935</dc:date>\n",
+>>>>>>> dev
        "    <dc:format>image/svg+xml</dc:format>\n",
        "    <dc:creator>\n",
        "     <cc:Agent>\n",
@@ -349,17 +546,29 @@
        "    <g id=\"xtick_1\">\n",
        "     <g id=\"line2d_1\">\n",
        "      <defs>\n",
+<<<<<<< HEAD
+       "       <path id=\"m4ff565e27b\" d=\"M 0 0 \n",
+=======
        "       <path id=\"md3b9ac63c3\" d=\"M 0 0 \n",
+>>>>>>> dev
        "L 0 6 \n",
        "\" style=\"stroke: #262626; stroke-width: 1.25\"/>\n",
        "      </defs>\n",
        "      <g>\n",
+<<<<<<< HEAD
+       "       <use xlink:href=\"#m4ff565e27b\" x=\"97.762547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+=======
        "       <use xlink:href=\"#md3b9ac63c3\" x=\"121.012547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+>>>>>>> dev
        "      </g>\n",
        "     </g>\n",
        "     <g id=\"text_1\">\n",
        "      <!-- 1.0 -->\n",
+<<<<<<< HEAD
+       "      <g style=\"fill: #262626\" transform=\"translate(88.578492 302.676195) scale(0.1155 -0.1155)\">\n",
+=======
        "      <g style=\"fill: #262626\" transform=\"translate(111.828492 302.676195) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <defs>\n",
        "        <path id=\"DejaVuSans-31\" d=\"M 794 531 \n",
        "L 1825 531 \n",
@@ -413,6 +622,25 @@
        "    <g id=\"xtick_2\">\n",
        "     <g id=\"line2d_2\">\n",
        "      <g>\n",
+<<<<<<< HEAD
+       "       <use xlink:href=\"#m4ff565e27b\" x=\"190.762547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_2\">\n",
+       "      <!-- 0.75 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(177.904148 302.676195) scale(0.1155 -0.1155)\">\n",
+       "       <defs>\n",
+       "        <path id=\"DejaVuSans-37\" d=\"M 525 4666 \n",
+       "L 3525 4666 \n",
+       "L 3525 4397 \n",
+       "L 1831 0 \n",
+       "L 1172 0 \n",
+       "L 2766 4134 \n",
+       "L 525 4134 \n",
+       "L 525 4666 \n",
+       "z\n",
+       "\" transform=\"scale(0.015625)\"/>\n",
+=======
        "       <use xlink:href=\"#md3b9ac63c3\" x=\"260.512547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
        "      </g>\n",
        "     </g>\n",
@@ -476,6 +704,7 @@
        "      <!-- 0.5 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(390.828492 302.676195) scale(0.1155 -0.1155)\">\n",
        "       <defs>\n",
+>>>>>>> dev
        "        <path id=\"DejaVuSans-35\" d=\"M 691 4666 \n",
        "L 3169 4666 \n",
        "L 3169 4134 \n",
@@ -504,6 +733,25 @@
        "       </defs>\n",
        "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
+<<<<<<< HEAD
+       "       <use xlink:href=\"#DejaVuSans-37\" x=\"95.410156\"/>\n",
+       "       <use xlink:href=\"#DejaVuSans-35\" x=\"159.033203\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "    </g>\n",
+       "    <g id=\"xtick_3\">\n",
+       "     <g id=\"line2d_3\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m4ff565e27b\" x=\"283.762547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_3\">\n",
+       "      <!-- 0.5 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(274.578492 302.676195) scale(0.1155 -0.1155)\">\n",
+       "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
+       "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
+=======
+>>>>>>> dev
        "       <use xlink:href=\"#DejaVuSans-35\" x=\"95.410156\"/>\n",
        "      </g>\n",
        "     </g>\n",
@@ -511,12 +759,63 @@
        "    <g id=\"xtick_4\">\n",
        "     <g id=\"line2d_4\">\n",
        "      <g>\n",
+<<<<<<< HEAD
+       "       <use xlink:href=\"#m4ff565e27b\" x=\"376.762547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_4\">\n",
+       "      <!-- 0.25 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(363.904148 302.676195) scale(0.1155 -0.1155)\">\n",
+       "       <defs>\n",
+       "        <path id=\"DejaVuSans-32\" d=\"M 1228 531 \n",
+       "L 3431 531 \n",
+       "L 3431 0 \n",
+       "L 469 0 \n",
+       "L 469 531 \n",
+       "Q 828 903 1448 1529 \n",
+       "Q 2069 2156 2228 2338 \n",
+       "Q 2531 2678 2651 2914 \n",
+       "Q 2772 3150 2772 3378 \n",
+       "Q 2772 3750 2511 3984 \n",
+       "Q 2250 4219 1831 4219 \n",
+       "Q 1534 4219 1204 4116 \n",
+       "Q 875 4013 500 3803 \n",
+       "L 500 4441 \n",
+       "Q 881 4594 1212 4672 \n",
+       "Q 1544 4750 1819 4750 \n",
+       "Q 2544 4750 2975 4387 \n",
+       "Q 3406 4025 3406 3419 \n",
+       "Q 3406 3131 3298 2873 \n",
+       "Q 3191 2616 2906 2266 \n",
+       "Q 2828 2175 2409 1742 \n",
+       "Q 1991 1309 1228 531 \n",
+       "z\n",
+       "\" transform=\"scale(0.015625)\"/>\n",
+       "       </defs>\n",
+       "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
+       "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
+       "       <use xlink:href=\"#DejaVuSans-32\" x=\"95.410156\"/>\n",
+       "       <use xlink:href=\"#DejaVuSans-35\" x=\"159.033203\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "    </g>\n",
+       "    <g id=\"xtick_5\">\n",
+       "     <g id=\"line2d_5\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m4ff565e27b\" x=\"469.762547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_5\">\n",
+       "      <!-- 0.01 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(456.904148 302.676195) scale(0.1155 -0.1155)\">\n",
+=======
        "       <use xlink:href=\"#md3b9ac63c3\" x=\"539.512547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
        "      </g>\n",
        "     </g>\n",
        "     <g id=\"text_4\">\n",
        "      <!-- 0.01 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(526.654148 302.676195) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-30\" x=\"95.410156\"/>\n",
@@ -524,7 +823,26 @@
        "      </g>\n",
        "     </g>\n",
        "    </g>\n",
+<<<<<<< HEAD
+       "    <g id=\"xtick_6\">\n",
+       "     <g id=\"line2d_6\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m4ff565e27b\" x=\"562.762547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_6\">\n",
+       "      <!-- 0.0 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(553.578492 302.676195) scale(0.1155 -0.1155)\">\n",
+       "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
+       "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
+       "       <use xlink:href=\"#DejaVuSans-30\" x=\"95.410156\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "    </g>\n",
+       "    <g id=\"text_7\">\n",
+=======
        "    <g id=\"text_5\">\n",
+>>>>>>> dev
        "     <!-- guidance_density -->\n",
        "     <g style=\"fill: #262626\" transform=\"translate(275.0775 318.652266) scale(0.126 -0.126)\">\n",
        "      <defs>\n",
@@ -819,17 +1137,30 @@
        "   </g>\n",
        "   <g id=\"matplotlib.axis_2\">\n",
        "    <g id=\"ytick_1\">\n",
+<<<<<<< HEAD
+       "     <g id=\"line2d_7\">\n",
+       "      <defs>\n",
+       "       <path id=\"m46663daca6\" d=\"M 0 0 \n",
+=======
        "     <g id=\"line2d_5\">\n",
        "      <defs>\n",
        "       <path id=\"m8e1fc7783a\" d=\"M 0 0 \n",
+>>>>>>> dev
        "L -6 0 \n",
        "\" style=\"stroke: #262626; stroke-width: 1.25\"/>\n",
        "      </defs>\n",
        "      <g>\n",
+<<<<<<< HEAD
+       "       <use xlink:href=\"#m46663daca6\" x=\"51.262547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_8\">\n",
+=======
        "       <use xlink:href=\"#m8e1fc7783a\" x=\"51.262547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
        "      </g>\n",
        "     </g>\n",
        "     <g id=\"text_6\">\n",
+>>>>>>> dev
        "      <!-- 0.0 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(23.394438 288.788098) scale(0.1155 -0.1155)\">\n",
        "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
@@ -839,6 +1170,16 @@
        "     </g>\n",
        "    </g>\n",
        "    <g id=\"ytick_2\">\n",
+<<<<<<< HEAD
+       "     <g id=\"line2d_8\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m46663daca6\" x=\"51.262547\" y=\"252.392702\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_9\">\n",
+       "      <!-- 0.1 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 256.7808) scale(0.1155 -0.1155)\">\n",
+=======
        "     <g id=\"line2d_6\">\n",
        "      <g>\n",
        "       <use xlink:href=\"#m8e1fc7783a\" x=\"51.262547\" y=\"252.224996\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
@@ -847,6 +1188,7 @@
        "     <g id=\"text_7\">\n",
        "      <!-- 0.1 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(23.394438 256.613094) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-31\" x=\"95.410156\"/>\n",
@@ -854,6 +1196,16 @@
        "     </g>\n",
        "    </g>\n",
        "    <g id=\"ytick_3\">\n",
+<<<<<<< HEAD
+       "     <g id=\"line2d_9\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m46663daca6\" x=\"51.262547\" y=\"220.385405\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_10\">\n",
+       "      <!-- 0.2 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 224.773502) scale(0.1155 -0.1155)\">\n",
+=======
        "     <g id=\"line2d_7\">\n",
        "      <g>\n",
        "       <use xlink:href=\"#m8e1fc7783a\" x=\"51.262547\" y=\"220.049992\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
@@ -888,6 +1240,7 @@
        "z\n",
        "\" transform=\"scale(0.015625)\"/>\n",
        "       </defs>\n",
+>>>>>>> dev
        "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-32\" x=\"95.410156\"/>\n",
@@ -895,6 +1248,16 @@
        "     </g>\n",
        "    </g>\n",
        "    <g id=\"ytick_4\">\n",
+<<<<<<< HEAD
+       "     <g id=\"line2d_10\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m46663daca6\" x=\"51.262547\" y=\"188.378107\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_11\">\n",
+       "      <!-- 0.3 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 192.766205) scale(0.1155 -0.1155)\">\n",
+=======
        "     <g id=\"line2d_8\">\n",
        "      <g>\n",
        "       <use xlink:href=\"#m8e1fc7783a\" x=\"51.262547\" y=\"187.874988\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
@@ -903,6 +1266,7 @@
        "     <g id=\"text_9\">\n",
        "      <!-- 0.3 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(23.394438 192.263085) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <defs>\n",
        "        <path id=\"DejaVuSans-33\" d=\"M 2597 2516 \n",
        "Q 3050 2419 3304 2112 \n",
@@ -944,6 +1308,16 @@
        "     </g>\n",
        "    </g>\n",
        "    <g id=\"ytick_5\">\n",
+<<<<<<< HEAD
+       "     <g id=\"line2d_11\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m46663daca6\" x=\"51.262547\" y=\"156.37081\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_12\">\n",
+       "      <!-- 0.4 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 160.758907) scale(0.1155 -0.1155)\">\n",
+=======
        "     <g id=\"line2d_9\">\n",
        "      <g>\n",
        "       <use xlink:href=\"#m8e1fc7783a\" x=\"51.262547\" y=\"155.699983\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
@@ -952,6 +1326,7 @@
        "     <g id=\"text_10\">\n",
        "      <!-- 0.4 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(23.394438 160.088081) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <defs>\n",
        "        <path id=\"DejaVuSans-34\" d=\"M 2419 4116 \n",
        "L 825 1625 \n",
@@ -980,6 +1355,16 @@
        "     </g>\n",
        "    </g>\n",
        "    <g id=\"ytick_6\">\n",
+<<<<<<< HEAD
+       "     <g id=\"line2d_12\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m46663daca6\" x=\"51.262547\" y=\"124.363512\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_13\">\n",
+       "      <!-- 0.5 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 128.75161) scale(0.1155 -0.1155)\">\n",
+=======
        "     <g id=\"line2d_10\">\n",
        "      <g>\n",
        "       <use xlink:href=\"#m8e1fc7783a\" x=\"51.262547\" y=\"123.524979\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
@@ -988,6 +1373,7 @@
        "     <g id=\"text_11\">\n",
        "      <!-- 0.5 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(23.394438 127.913077) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-35\" x=\"95.410156\"/>\n",
@@ -995,6 +1381,16 @@
        "     </g>\n",
        "    </g>\n",
        "    <g id=\"ytick_7\">\n",
+<<<<<<< HEAD
+       "     <g id=\"line2d_13\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m46663daca6\" x=\"51.262547\" y=\"92.356214\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_14\">\n",
+       "      <!-- 0.6 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 96.744312) scale(0.1155 -0.1155)\">\n",
+=======
        "     <g id=\"line2d_11\">\n",
        "      <g>\n",
        "       <use xlink:href=\"#m8e1fc7783a\" x=\"51.262547\" y=\"91.349975\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
@@ -1003,6 +1399,7 @@
        "     <g id=\"text_12\">\n",
        "      <!-- 0.6 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(23.394438 95.738073) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <defs>\n",
        "        <path id=\"DejaVuSans-36\" d=\"M 2113 2584 \n",
        "Q 1688 2584 1439 2293 \n",
@@ -1042,6 +1439,16 @@
        "     </g>\n",
        "    </g>\n",
        "    <g id=\"ytick_8\">\n",
+<<<<<<< HEAD
+       "     <g id=\"line2d_14\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m46663daca6\" x=\"51.262547\" y=\"60.348917\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_15\">\n",
+       "      <!-- 0.7 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 64.737014) scale(0.1155 -0.1155)\">\n",
+=======
        "     <g id=\"line2d_12\">\n",
        "      <g>\n",
        "       <use xlink:href=\"#m8e1fc7783a\" x=\"51.262547\" y=\"59.174971\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
@@ -1062,6 +1469,7 @@
        "z\n",
        "\" transform=\"scale(0.015625)\"/>\n",
        "       </defs>\n",
+>>>>>>> dev
        "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-37\" x=\"95.410156\"/>\n",
@@ -1069,6 +1477,57 @@
        "     </g>\n",
        "    </g>\n",
        "    <g id=\"ytick_9\">\n",
+<<<<<<< HEAD
+       "     <g id=\"line2d_15\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m46663daca6\" x=\"51.262547\" y=\"28.341619\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_16\">\n",
+       "      <!-- 0.8 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 32.729717) scale(0.1155 -0.1155)\">\n",
+       "       <defs>\n",
+       "        <path id=\"DejaVuSans-38\" d=\"M 2034 2216 \n",
+       "Q 1584 2216 1326 1975 \n",
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+       "z\n",
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+       "Q 3634 634 3220 271 \n",
+       "Q 2806 -91 2034 -91 \n",
+       "Q 1263 -91 848 271 \n",
+       "Q 434 634 434 1313 \n",
+       "Q 434 1759 690 2068 \n",
+       "Q 947 2378 1403 2484 \n",
+       "z\n",
+       "M 1172 3481 \n",
+       "Q 1172 3119 1398 2916 \n",
+       "Q 1625 2713 2034 2713 \n",
+       "Q 2441 2713 2670 2916 \n",
+       "Q 2900 3119 2900 3481 \n",
+       "Q 2900 3844 2670 4047 \n",
+       "Q 2441 4250 2034 4250 \n",
+       "Q 1625 4250 1398 4047 \n",
+       "Q 1172 3844 1172 3481 \n",
+       "z\n",
+       "\" transform=\"scale(0.015625)\"/>\n",
+       "       </defs>\n",
+=======
        "     <g id=\"line2d_13\">\n",
        "      <g>\n",
        "       <use xlink:href=\"#m8e1fc7783a\" x=\"51.262547\" y=\"26.999967\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
@@ -1077,13 +1536,18 @@
        "     <g id=\"text_14\">\n",
        "      <!-- 0.8 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(23.394438 31.388065) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-38\" x=\"95.410156\"/>\n",
        "      </g>\n",
        "     </g>\n",
        "    </g>\n",
+<<<<<<< HEAD
+       "    <g id=\"text_17\">\n",
+=======
        "    <g id=\"text_15\">\n",
+>>>>>>> dev
        "     <!-- metametric -->\n",
        "     <g style=\"fill: #262626\" transform=\"translate(16.774031 182.430563) rotate(-90) scale(0.126 -0.126)\">\n",
        "      <defs>\n",
@@ -1149,6 +1613,86 @@
        "    </g>\n",
        "   </g>\n",
        "   <g id=\"patch_3\">\n",
+<<<<<<< HEAD
+       "    <path d=\"M 60.562547 284.4 \n",
+       "L 134.962547 284.4 \n",
+       "L 134.962547 60.817948 \n",
+       "L 60.562547 60.817948 \n",
+       "z\n",
+       "\" clip-path=\"url(#p2811523b5c)\" style=\"fill: #5875a4; stroke: #ffffff; stroke-linejoin: miter\"/>\n",
+       "   </g>\n",
+       "   <g id=\"patch_4\">\n",
+       "    <path d=\"M 153.562547 284.4 \n",
+       "L 227.962547 284.4 \n",
+       "L 227.962547 62.722705 \n",
+       "L 153.562547 62.722705 \n",
+       "z\n",
+       "\" clip-path=\"url(#p2811523b5c)\" style=\"fill: #5875a4; stroke: #ffffff; stroke-linejoin: miter\"/>\n",
+       "   </g>\n",
+       "   <g id=\"patch_5\">\n",
+       "    <path d=\"M 246.562547 284.4 \n",
+       "L 320.962547 284.4 \n",
+       "L 320.962547 59.900553 \n",
+       "L 246.562547 59.900553 \n",
+       "z\n",
+       "\" clip-path=\"url(#p2811523b5c)\" style=\"fill: #5875a4; stroke: #ffffff; stroke-linejoin: miter\"/>\n",
+       "   </g>\n",
+       "   <g id=\"patch_6\">\n",
+       "    <path d=\"M 339.562547 284.4 \n",
+       "L 413.962547 284.4 \n",
+       "L 413.962547 56.612418 \n",
+       "L 339.562547 56.612418 \n",
+       "z\n",
+       "\" clip-path=\"url(#p2811523b5c)\" style=\"fill: #5875a4; stroke: #ffffff; stroke-linejoin: miter\"/>\n",
+       "   </g>\n",
+       "   <g id=\"patch_7\">\n",
+       "    <path d=\"M 432.562547 284.4 \n",
+       "L 506.962547 284.4 \n",
+       "L 506.962547 59.13338 \n",
+       "L 432.562547 59.13338 \n",
+       "z\n",
+       "\" clip-path=\"url(#p2811523b5c)\" style=\"fill: #5875a4; stroke: #ffffff; stroke-linejoin: miter\"/>\n",
+       "   </g>\n",
+       "   <g id=\"patch_8\">\n",
+       "    <path d=\"M 525.562547 284.4 \n",
+       "L 599.962547 284.4 \n",
+       "L 599.962547 59.087837 \n",
+       "L 525.562547 59.087837 \n",
+       "z\n",
+       "\" clip-path=\"url(#p2811523b5c)\" style=\"fill: #5875a4; stroke: #ffffff; stroke-linejoin: miter\"/>\n",
+       "   </g>\n",
+       "   <g id=\"line2d_16\">\n",
+       "    <path d=\"M 97.762547 97.145938 \n",
+       "L 97.762547 24.489958 \n",
+       "\" clip-path=\"url(#p2811523b5c)\" style=\"fill: none; stroke: #424242; stroke-width: 2.25; stroke-linecap: round\"/>\n",
+       "   </g>\n",
+       "   <g id=\"line2d_17\">\n",
+       "    <path d=\"M 190.762547 103.627368 \n",
+       "L 190.762547 21.818041 \n",
+       "\" clip-path=\"url(#p2811523b5c)\" style=\"fill: none; stroke: #424242; stroke-width: 2.25; stroke-linecap: round\"/>\n",
+       "   </g>\n",
+       "   <g id=\"line2d_18\">\n",
+       "    <path d=\"M 283.762547 97.957876 \n",
+       "L 283.762547 21.843231 \n",
+       "\" clip-path=\"url(#p2811523b5c)\" style=\"fill: none; stroke: #424242; stroke-width: 2.25; stroke-linecap: round\"/>\n",
+       "   </g>\n",
+       "   <g id=\"line2d_19\">\n",
+       "    <path d=\"M 376.762547 92.824836 \n",
+       "L 376.762547 20.4 \n",
+       "\" clip-path=\"url(#p2811523b5c)\" style=\"fill: none; stroke: #424242; stroke-width: 2.25; stroke-linecap: round\"/>\n",
+       "   </g>\n",
+       "   <g id=\"line2d_20\">\n",
+       "    <path d=\"M 469.762547 93.247074 \n",
+       "L 469.762547 25.019685 \n",
+       "\" clip-path=\"url(#p2811523b5c)\" style=\"fill: none; stroke: #424242; stroke-width: 2.25; stroke-linecap: round\"/>\n",
+       "   </g>\n",
+       "   <g id=\"line2d_21\">\n",
+       "    <path d=\"M 562.762547 93.112614 \n",
+       "L 562.762547 25.06306 \n",
+       "\" clip-path=\"url(#p2811523b5c)\" style=\"fill: none; stroke: #424242; stroke-width: 2.25; stroke-linecap: round\"/>\n",
+       "   </g>\n",
+       "   <g id=\"patch_9\">\n",
+=======
        "    <path d=\"M 65.212547 284.4 \n",
        "L 176.812547 284.4 \n",
        "L 176.812547 56.393766 \n",
@@ -1201,15 +1745,25 @@
        "\" clip-path=\"url(#pd6fc07ee9a)\" style=\"fill: none; stroke: #424242; stroke-width: 2.25; stroke-linecap: round\"/>\n",
        "   </g>\n",
        "   <g id=\"patch_7\">\n",
+>>>>>>> dev
        "    <path d=\"M 51.262547 284.4 \n",
        "L 51.262547 7.2 \n",
        "\" style=\"fill: none; stroke: #262626; stroke-width: 1.25; stroke-linejoin: miter; stroke-linecap: square\"/>\n",
        "   </g>\n",
+<<<<<<< HEAD
+       "   <g id=\"patch_10\">\n",
+=======
        "   <g id=\"patch_8\">\n",
+>>>>>>> dev
        "    <path d=\"M 51.262547 284.4 \n",
        "L 609.262547 284.4 \n",
        "\" style=\"fill: none; stroke: #262626; stroke-width: 1.25; stroke-linejoin: miter; stroke-linecap: square\"/>\n",
        "   </g>\n",
+<<<<<<< HEAD
+       "   <g id=\"text_18\">\n",
+       "    <!-- 0.698535 -->\n",
+       "    <g style=\"fill: #262626\" transform=\"translate(73.904734 58.73826) scale(0.1 -0.1)\">\n",
+=======
        "   <g id=\"text_16\">\n",
        "    <!-- 0.708644 -->\n",
        "    <g style=\"fill: #262626\" transform=\"translate(97.154734 54.314078) scale(0.1 -0.1)\">\n",
@@ -1239,6 +1793,7 @@
        "   <g id=\"text_18\">\n",
        "    <!-- 0.696928 -->\n",
        "    <g style=\"fill: #262626\" transform=\"translate(376.154734 58.083809) scale(0.1 -0.1)\">\n",
+>>>>>>> dev
        "     <defs>\n",
        "      <path id=\"DejaVuSans-39\" d=\"M 703 97 \n",
        "L 703 672 \n",
@@ -1275,6 +1830,17 @@
        "     <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
        "     <use xlink:href=\"#DejaVuSans-36\" x=\"95.410156\"/>\n",
        "     <use xlink:href=\"#DejaVuSans-39\" x=\"159.033203\"/>\n",
+<<<<<<< HEAD
+       "     <use xlink:href=\"#DejaVuSans-38\" x=\"222.65625\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-35\" x=\"286.279297\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-33\" x=\"349.902344\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-35\" x=\"413.525391\"/>\n",
+       "    </g>\n",
+       "   </g>\n",
+       "   <g id=\"text_19\">\n",
+       "    <!-- 0.692584 -->\n",
+       "    <g style=\"fill: #262626\" transform=\"translate(166.904734 60.643017) scale(0.1 -0.1)\">\n",
+=======
        "     <use xlink:href=\"#DejaVuSans-36\" x=\"222.65625\"/>\n",
        "     <use xlink:href=\"#DejaVuSans-39\" x=\"286.279297\"/>\n",
        "     <use xlink:href=\"#DejaVuSans-32\" x=\"349.902344\"/>\n",
@@ -1284,20 +1850,83 @@
        "   <g id=\"text_19\">\n",
        "    <!-- 0.693012 -->\n",
        "    <g style=\"fill: #262626\" transform=\"translate(515.654734 59.343667) scale(0.1 -0.1)\">\n",
+>>>>>>> dev
        "     <use xlink:href=\"#DejaVuSans-30\"/>\n",
        "     <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
        "     <use xlink:href=\"#DejaVuSans-36\" x=\"95.410156\"/>\n",
        "     <use xlink:href=\"#DejaVuSans-39\" x=\"159.033203\"/>\n",
+<<<<<<< HEAD
+       "     <use xlink:href=\"#DejaVuSans-32\" x=\"222.65625\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-35\" x=\"286.279297\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-38\" x=\"349.902344\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-34\" x=\"413.525391\"/>\n",
+       "    </g>\n",
+       "   </g>\n",
+       "   <g id=\"text_20\">\n",
+       "    <!-- 0.701401 -->\n",
+       "    <g style=\"fill: #262626\" transform=\"translate(259.904734 57.820866) scale(0.1 -0.1)\">\n",
+       "     <use xlink:href=\"#DejaVuSans-30\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-37\" x=\"95.410156\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-30\" x=\"159.033203\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-31\" x=\"222.65625\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-34\" x=\"286.279297\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-30\" x=\"349.902344\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-31\" x=\"413.525391\"/>\n",
+       "    </g>\n",
+       "   </g>\n",
+       "   <g id=\"text_21\">\n",
+       "    <!-- 0.711674 -->\n",
+       "    <g style=\"fill: #262626\" transform=\"translate(352.904734 54.532731) scale(0.1 -0.1)\">\n",
+       "     <use xlink:href=\"#DejaVuSans-30\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-37\" x=\"95.410156\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-31\" x=\"159.033203\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-31\" x=\"222.65625\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-36\" x=\"286.279297\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-37\" x=\"349.902344\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-34\" x=\"413.525391\"/>\n",
+       "    </g>\n",
+       "   </g>\n",
+       "   <g id=\"text_22\">\n",
+       "    <!-- 0.703798 -->\n",
+       "    <g style=\"fill: #262626\" transform=\"translate(445.904734 57.053692) scale(0.1 -0.1)\">\n",
+       "     <use xlink:href=\"#DejaVuSans-30\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-37\" x=\"95.410156\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-30\" x=\"159.033203\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-33\" x=\"222.65625\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-37\" x=\"286.279297\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-39\" x=\"349.902344\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-38\" x=\"413.525391\"/>\n",
+       "    </g>\n",
+       "   </g>\n",
+       "   <g id=\"text_23\">\n",
+       "    <!-- 0.70394 -->\n",
+       "    <g style=\"fill: #262626\" transform=\"translate(542.085984 57.008149) scale(0.1 -0.1)\">\n",
+       "     <use xlink:href=\"#DejaVuSans-30\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-37\" x=\"95.410156\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-30\" x=\"159.033203\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-33\" x=\"222.65625\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-39\" x=\"286.279297\"/>\n",
+       "     <use xlink:href=\"#DejaVuSans-34\" x=\"349.902344\"/>\n",
+=======
        "     <use xlink:href=\"#DejaVuSans-33\" x=\"222.65625\"/>\n",
        "     <use xlink:href=\"#DejaVuSans-30\" x=\"286.279297\"/>\n",
        "     <use xlink:href=\"#DejaVuSans-31\" x=\"349.902344\"/>\n",
        "     <use xlink:href=\"#DejaVuSans-32\" x=\"413.525391\"/>\n",
+>>>>>>> dev
        "    </g>\n",
        "   </g>\n",
        "  </g>\n",
        " </g>\n",
        " <defs>\n",
+<<<<<<< HEAD
+       "  <clipPath id=\"p2811523b5c\">\n",
+=======
        "  <clipPath id=\"pd6fc07ee9a\">\n",
+>>>>>>> dev
        "   <rect x=\"51.262547\" y=\"7.2\" width=\"558\" height=\"277.2\"/>\n",
        "  </clipPath>\n",
        " </defs>\n",
@@ -1320,7 +1949,11 @@
     "    data=df,\n",
     "    x=\"guidance_density\",\n",
     "    y=\"metametric\",\n",
+<<<<<<< HEAD
+    "    order=ORDER,\n",
+=======
     "    order=[1.0, 0.8, 0.5, 0.01],\n",
+>>>>>>> dev
     "    errorbar=\"sd\",\n",
     ")\n",
     "axs.bar_label(axs.containers[0], fontsize=10)\n",
@@ -1329,7 +1962,11 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 70,
+=======
    "execution_count": null,
+>>>>>>> dev
    "id": "458c0799",
    "metadata": {},
    "outputs": [
@@ -1339,12 +1976,20 @@
        "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
        "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
        "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+<<<<<<< HEAD
+       "<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"616.462547pt\" height=\"328.823109pt\" viewBox=\"0 0 616.462547 328.823109\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n",
+=======
        "<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"616.462547pt\" height=\"331.280075pt\" viewBox=\"0 0 616.462547 331.280075\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n",
+>>>>>>> dev
        " <metadata>\n",
        "  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n",
        "   <cc:Work>\n",
        "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
+<<<<<<< HEAD
+       "    <dc:date>2025-05-14T22:16:24.169365</dc:date>\n",
+=======
        "    <dc:date>2025-05-14T17:27:15.688787</dc:date>\n",
+>>>>>>> dev
        "    <dc:format>image/svg+xml</dc:format>\n",
        "    <dc:creator>\n",
        "     <cc:Agent>\n",
@@ -1359,21 +2004,37 @@
        " </defs>\n",
        " <g id=\"figure_1\">\n",
        "  <g id=\"patch_1\">\n",
+<<<<<<< HEAD
+       "   <path d=\"M 0 328.823109 \n",
+       "L 616.462547 328.823109 \n",
+       "L 616.462547 0 \n",
+       "L 0 0 \n",
+       "L 0 328.823109 \n",
+=======
        "   <path d=\"M 0 331.280075 \n",
        "L 616.462547 331.280075 \n",
        "L 616.462547 0 \n",
        "L 0 0 \n",
        "L 0 331.280075 \n",
+>>>>>>> dev
        "z\n",
        "\" style=\"fill: none\"/>\n",
        "  </g>\n",
        "  <g id=\"axes_1\">\n",
        "   <g id=\"patch_2\">\n",
+<<<<<<< HEAD
+       "    <path d=\"M 51.262547 284.4 \n",
+       "L 609.262547 284.4 \n",
+       "L 609.262547 7.2 \n",
+       "L 51.262547 7.2 \n",
+       "L 51.262547 284.4 \n",
+=======
        "    <path d=\"M 51.262547 286.856966 \n",
        "L 609.262547 286.856966 \n",
        "L 609.262547 9.656966 \n",
        "L 51.262547 9.656966 \n",
        "L 51.262547 286.856966 \n",
+>>>>>>> dev
        "z\n",
        "\" style=\"fill: none\"/>\n",
        "   </g>\n",
@@ -1381,17 +2042,29 @@
        "    <g id=\"xtick_1\">\n",
        "     <g id=\"line2d_1\">\n",
        "      <defs>\n",
+<<<<<<< HEAD
+       "       <path id=\"m3ba1ea3388\" d=\"M 0 0 \n",
+=======
        "       <path id=\"m147d3978dd\" d=\"M 0 0 \n",
+>>>>>>> dev
        "L 0 6 \n",
        "\" style=\"stroke: #262626; stroke-width: 1.25\"/>\n",
        "      </defs>\n",
        "      <g>\n",
+<<<<<<< HEAD
+       "       <use xlink:href=\"#m3ba1ea3388\" x=\"97.762547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+=======
        "       <use xlink:href=\"#m147d3978dd\" x=\"107.062547\" y=\"286.856966\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+>>>>>>> dev
        "      </g>\n",
        "     </g>\n",
        "     <g id=\"text_1\">\n",
        "      <!-- 1.0 -->\n",
+<<<<<<< HEAD
+       "      <g style=\"fill: #262626\" transform=\"translate(88.578492 302.676195) scale(0.1155 -0.1155)\">\n",
+=======
        "      <g style=\"fill: #262626\" transform=\"translate(97.878492 305.133161) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <defs>\n",
        "        <path id=\"DejaVuSans-31\" d=\"M 794 531 \n",
        "L 1825 531 \n",
@@ -1445,6 +2118,25 @@
        "    <g id=\"xtick_2\">\n",
        "     <g id=\"line2d_2\">\n",
        "      <g>\n",
+<<<<<<< HEAD
+       "       <use xlink:href=\"#m3ba1ea3388\" x=\"190.762547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_2\">\n",
+       "      <!-- 0.75 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(177.904148 302.676195) scale(0.1155 -0.1155)\">\n",
+       "       <defs>\n",
+       "        <path id=\"DejaVuSans-37\" d=\"M 525 4666 \n",
+       "L 3525 4666 \n",
+       "L 3525 4397 \n",
+       "L 1831 0 \n",
+       "L 1172 0 \n",
+       "L 2766 4134 \n",
+       "L 525 4134 \n",
+       "L 525 4666 \n",
+       "z\n",
+       "\" transform=\"scale(0.015625)\"/>\n",
+=======
        "       <use xlink:href=\"#m147d3978dd\" x=\"218.662547\" y=\"286.856966\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
        "      </g>\n",
        "     </g>\n",
@@ -1508,6 +2200,7 @@
        "      <!-- 0.5 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(321.078492 305.133161) scale(0.1155 -0.1155)\">\n",
        "       <defs>\n",
+>>>>>>> dev
        "        <path id=\"DejaVuSans-35\" d=\"M 691 4666 \n",
        "L 3169 4666 \n",
        "L 3169 4134 \n",
@@ -1536,6 +2229,25 @@
        "       </defs>\n",
        "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
+<<<<<<< HEAD
+       "       <use xlink:href=\"#DejaVuSans-37\" x=\"95.410156\"/>\n",
+       "       <use xlink:href=\"#DejaVuSans-35\" x=\"159.033203\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "    </g>\n",
+       "    <g id=\"xtick_3\">\n",
+       "     <g id=\"line2d_3\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m3ba1ea3388\" x=\"283.762547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_3\">\n",
+       "      <!-- 0.5 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(274.578492 302.676195) scale(0.1155 -0.1155)\">\n",
+       "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
+       "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
+=======
+>>>>>>> dev
        "       <use xlink:href=\"#DejaVuSans-35\" x=\"95.410156\"/>\n",
        "      </g>\n",
        "     </g>\n",
@@ -1543,12 +2255,21 @@
        "    <g id=\"xtick_4\">\n",
        "     <g id=\"line2d_4\">\n",
        "      <g>\n",
+<<<<<<< HEAD
+       "       <use xlink:href=\"#m3ba1ea3388\" x=\"376.762547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_4\">\n",
+       "      <!-- 0.25 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(363.904148 302.676195) scale(0.1155 -0.1155)\">\n",
+=======
        "       <use xlink:href=\"#m147d3978dd\" x=\"441.862547\" y=\"286.856966\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
        "      </g>\n",
        "     </g>\n",
        "     <g id=\"text_4\">\n",
        "      <!-- 0.2 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(432.678492 305.133161) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <defs>\n",
        "        <path id=\"DejaVuSans-32\" d=\"M 1228 531 \n",
        "L 3431 531 \n",
@@ -1578,18 +2299,30 @@
        "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-32\" x=\"95.410156\"/>\n",
+<<<<<<< HEAD
+       "       <use xlink:href=\"#DejaVuSans-35\" x=\"159.033203\"/>\n",
+=======
+>>>>>>> dev
        "      </g>\n",
        "     </g>\n",
        "    </g>\n",
        "    <g id=\"xtick_5\">\n",
        "     <g id=\"line2d_5\">\n",
        "      <g>\n",
+<<<<<<< HEAD
+       "       <use xlink:href=\"#m3ba1ea3388\" x=\"469.762547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+=======
        "       <use xlink:href=\"#m147d3978dd\" x=\"553.462547\" y=\"286.856966\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+>>>>>>> dev
        "      </g>\n",
        "     </g>\n",
        "     <g id=\"text_5\">\n",
        "      <!-- 0.01 -->\n",
+<<<<<<< HEAD
+       "      <g style=\"fill: #262626\" transform=\"translate(456.904148 302.676195) scale(0.1155 -0.1155)\">\n",
+=======
        "      <g style=\"fill: #262626\" transform=\"translate(540.604148 305.133161) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-30\" x=\"95.410156\"/>\n",
@@ -1597,9 +2330,30 @@
        "      </g>\n",
        "     </g>\n",
        "    </g>\n",
+<<<<<<< HEAD
+       "    <g id=\"xtick_6\">\n",
+       "     <g id=\"line2d_6\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m3ba1ea3388\" x=\"562.762547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_6\">\n",
+       "      <!-- 0.0 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(553.578492 302.676195) scale(0.1155 -0.1155)\">\n",
+       "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
+       "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
+       "       <use xlink:href=\"#DejaVuSans-30\" x=\"95.410156\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "    </g>\n",
+       "    <g id=\"text_7\">\n",
+       "     <!-- guidance_density -->\n",
+       "     <g style=\"fill: #262626\" transform=\"translate(275.0775 318.652266) scale(0.126 -0.126)\">\n",
+=======
        "    <g id=\"text_6\">\n",
        "     <!-- guidance_density -->\n",
        "     <g style=\"fill: #262626\" transform=\"translate(275.0775 321.109231) scale(0.126 -0.126)\">\n",
+>>>>>>> dev
        "      <defs>\n",
        "       <path id=\"DejaVuSans-67\" d=\"M 2906 1791 \n",
        "Q 2906 2416 2648 2759 \n",
@@ -1892,6 +2646,16 @@
        "   </g>\n",
        "   <g id=\"matplotlib.axis_2\">\n",
        "    <g id=\"ytick_1\">\n",
+<<<<<<< HEAD
+       "     <g id=\"line2d_7\">\n",
+       "      <path d=\"M 51.262547 267.237974 \n",
+       "L 609.262547 267.237974 \n",
+       "\" clip-path=\"url(#pbd6c032448)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
+       "     </g>\n",
+       "     <g id=\"line2d_8\">\n",
+       "      <defs>\n",
+       "       <path id=\"m6d89456a14\" d=\"M 0 0 \n",
+=======
        "     <g id=\"line2d_6\">\n",
        "      <path d=\"M 51.262547 246.929017 \n",
        "L 609.262547 246.929017 \n",
@@ -1900,16 +2664,80 @@
        "     <g id=\"line2d_7\">\n",
        "      <defs>\n",
        "       <path id=\"m370ca855b7\" d=\"M 0 0 \n",
+>>>>>>> dev
        "L -6 0 \n",
        "\" style=\"stroke: #262626; stroke-width: 1.25\"/>\n",
        "      </defs>\n",
        "      <g>\n",
+<<<<<<< HEAD
+       "       <use xlink:href=\"#m6d89456a14\" x=\"51.262547\" y=\"267.237974\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_8\">\n",
+       "      <!-- 0.3 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 271.626072) scale(0.1155 -0.1155)\">\n",
+       "       <defs>\n",
+       "        <path id=\"DejaVuSans-33\" d=\"M 2597 2516 \n",
+       "Q 3050 2419 3304 2112 \n",
+       "Q 3559 1806 3559 1356 \n",
+       "Q 3559 666 3084 287 \n",
+       "Q 2609 -91 1734 -91 \n",
+       "Q 1441 -91 1130 -33 \n",
+       "Q 819 25 488 141 \n",
+       "L 488 750 \n",
+       "Q 750 597 1062 519 \n",
+       "Q 1375 441 1716 441 \n",
+       "Q 2309 441 2620 675 \n",
+       "Q 2931 909 2931 1356 \n",
+       "Q 2931 1769 2642 2001 \n",
+       "Q 2353 2234 1838 2234 \n",
+       "L 1294 2234 \n",
+       "L 1294 2753 \n",
+       "L 1863 2753 \n",
+       "Q 2328 2753 2575 2939 \n",
+       "Q 2822 3125 2822 3475 \n",
+       "Q 2822 3834 2567 4026 \n",
+       "Q 2313 4219 1838 4219 \n",
+       "Q 1578 4219 1281 4162 \n",
+       "Q 984 4106 628 3988 \n",
+       "L 628 4550 \n",
+       "Q 988 4650 1302 4700 \n",
+       "Q 1616 4750 1894 4750 \n",
+       "Q 2613 4750 3031 4423 \n",
+       "Q 3450 4097 3450 3541 \n",
+       "Q 3450 3153 3228 2886 \n",
+       "Q 3006 2619 2597 2516 \n",
+       "z\n",
+       "\" transform=\"scale(0.015625)\"/>\n",
+       "       </defs>\n",
+       "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
+       "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
+       "       <use xlink:href=\"#DejaVuSans-33\" x=\"95.410156\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "    </g>\n",
+       "    <g id=\"ytick_2\">\n",
+       "     <g id=\"line2d_9\">\n",
+       "      <path d=\"M 51.262547 225.984618 \n",
+       "L 609.262547 225.984618 \n",
+       "\" clip-path=\"url(#pbd6c032448)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
+       "     </g>\n",
+       "     <g id=\"line2d_10\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m6d89456a14\" x=\"51.262547\" y=\"225.984618\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_9\">\n",
+       "      <!-- 0.4 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 230.372716) scale(0.1155 -0.1155)\">\n",
+=======
        "       <use xlink:href=\"#m370ca855b7\" x=\"51.262547\" y=\"246.929017\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
        "      </g>\n",
        "     </g>\n",
        "     <g id=\"text_7\">\n",
        "      <!-- 0.4 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(23.394438 251.317115) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <defs>\n",
        "        <path id=\"DejaVuSans-34\" d=\"M 2419 4116 \n",
        "L 825 1625 \n",
@@ -1937,6 +2765,22 @@
        "      </g>\n",
        "     </g>\n",
        "    </g>\n",
+<<<<<<< HEAD
+       "    <g id=\"ytick_3\">\n",
+       "     <g id=\"line2d_11\">\n",
+       "      <path d=\"M 51.262547 184.731262 \n",
+       "L 609.262547 184.731262 \n",
+       "\" clip-path=\"url(#pbd6c032448)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
+       "     </g>\n",
+       "     <g id=\"line2d_12\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m6d89456a14\" x=\"51.262547\" y=\"184.731262\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_10\">\n",
+       "      <!-- 0.5 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 189.11936) scale(0.1155 -0.1155)\">\n",
+=======
        "    <g id=\"ytick_2\">\n",
        "     <g id=\"line2d_8\">\n",
        "      <path d=\"M 51.262547 199.860833 \n",
@@ -1951,12 +2795,29 @@
        "     <g id=\"text_8\">\n",
        "      <!-- 0.5 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(23.394438 204.248931) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-35\" x=\"95.410156\"/>\n",
        "      </g>\n",
        "     </g>\n",
        "    </g>\n",
+<<<<<<< HEAD
+       "    <g id=\"ytick_4\">\n",
+       "     <g id=\"line2d_13\">\n",
+       "      <path d=\"M 51.262547 143.477906 \n",
+       "L 609.262547 143.477906 \n",
+       "\" clip-path=\"url(#pbd6c032448)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
+       "     </g>\n",
+       "     <g id=\"line2d_14\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m6d89456a14\" x=\"51.262547\" y=\"143.477906\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_11\">\n",
+       "      <!-- 0.6 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 147.866004) scale(0.1155 -0.1155)\">\n",
+=======
        "    <g id=\"ytick_3\">\n",
        "     <g id=\"line2d_10\">\n",
        "      <path d=\"M 51.262547 152.792649 \n",
@@ -1971,6 +2832,7 @@
        "     <g id=\"text_9\">\n",
        "      <!-- 0.6 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(23.394438 157.180747) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <defs>\n",
        "        <path id=\"DejaVuSans-36\" d=\"M 2113 2584 \n",
        "Q 1688 2584 1439 2293 \n",
@@ -2009,6 +2871,22 @@
        "      </g>\n",
        "     </g>\n",
        "    </g>\n",
+<<<<<<< HEAD
+       "    <g id=\"ytick_5\">\n",
+       "     <g id=\"line2d_15\">\n",
+       "      <path d=\"M 51.262547 102.22455 \n",
+       "L 609.262547 102.22455 \n",
+       "\" clip-path=\"url(#pbd6c032448)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
+       "     </g>\n",
+       "     <g id=\"line2d_16\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m6d89456a14\" x=\"51.262547\" y=\"102.22455\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_12\">\n",
+       "      <!-- 0.7 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 106.612648) scale(0.1155 -0.1155)\">\n",
+=======
        "    <g id=\"ytick_4\">\n",
        "     <g id=\"line2d_12\">\n",
        "      <path d=\"M 51.262547 105.724465 \n",
@@ -2035,12 +2913,70 @@
        "z\n",
        "\" transform=\"scale(0.015625)\"/>\n",
        "       </defs>\n",
+>>>>>>> dev
        "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-37\" x=\"95.410156\"/>\n",
        "      </g>\n",
        "     </g>\n",
        "    </g>\n",
+<<<<<<< HEAD
+       "    <g id=\"ytick_6\">\n",
+       "     <g id=\"line2d_17\">\n",
+       "      <path d=\"M 51.262547 60.971195 \n",
+       "L 609.262547 60.971195 \n",
+       "\" clip-path=\"url(#pbd6c032448)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
+       "     </g>\n",
+       "     <g id=\"line2d_18\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m6d89456a14\" x=\"51.262547\" y=\"60.971195\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_13\">\n",
+       "      <!-- 0.8 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 65.359292) scale(0.1155 -0.1155)\">\n",
+       "       <defs>\n",
+       "        <path id=\"DejaVuSans-38\" d=\"M 2034 2216 \n",
+       "Q 1584 2216 1326 1975 \n",
+       "Q 1069 1734 1069 1313 \n",
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+       "Q 1584 409 2034 409 \n",
+       "Q 2484 409 2743 651 \n",
+       "Q 3003 894 3003 1313 \n",
+       "Q 3003 1734 2745 1975 \n",
+       "Q 2488 2216 2034 2216 \n",
+       "z\n",
+       "M 1403 2484 \n",
+       "Q 997 2584 770 2862 \n",
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+       "Q 544 4100 942 4425 \n",
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+       "Q 3525 3141 3298 2862 \n",
+       "Q 3072 2584 2669 2484 \n",
+       "Q 3125 2378 3379 2068 \n",
+       "Q 3634 1759 3634 1313 \n",
+       "Q 3634 634 3220 271 \n",
+       "Q 2806 -91 2034 -91 \n",
+       "Q 1263 -91 848 271 \n",
+       "Q 434 634 434 1313 \n",
+       "Q 434 1759 690 2068 \n",
+       "Q 947 2378 1403 2484 \n",
+       "z\n",
+       "M 1172 3481 \n",
+       "Q 1172 3119 1398 2916 \n",
+       "Q 1625 2713 2034 2713 \n",
+       "Q 2441 2713 2670 2916 \n",
+       "Q 2900 3119 2900 3481 \n",
+       "Q 2900 3844 2670 4047 \n",
+       "Q 2441 4250 2034 4250 \n",
+       "Q 1625 4250 1398 4047 \n",
+       "Q 1172 3844 1172 3481 \n",
+       "z\n",
+       "\" transform=\"scale(0.015625)\"/>\n",
+       "       </defs>\n",
+=======
        "    <g id=\"ytick_5\">\n",
        "     <g id=\"line2d_14\">\n",
        "      <path d=\"M 51.262547 58.656282 \n",
@@ -2055,12 +2991,29 @@
        "     <g id=\"text_11\">\n",
        "      <!-- 0.8 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(23.394438 63.044379) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-38\" x=\"95.410156\"/>\n",
        "      </g>\n",
        "     </g>\n",
        "    </g>\n",
+<<<<<<< HEAD
+       "    <g id=\"ytick_7\">\n",
+       "     <g id=\"line2d_19\">\n",
+       "      <path d=\"M 51.262547 19.717839 \n",
+       "L 609.262547 19.717839 \n",
+       "\" clip-path=\"url(#pbd6c032448)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
+       "     </g>\n",
+       "     <g id=\"line2d_20\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m6d89456a14\" x=\"51.262547\" y=\"19.717839\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_14\">\n",
+       "      <!-- 0.9 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 24.105936) scale(0.1155 -0.1155)\">\n",
+=======
        "    <g id=\"ytick_6\">\n",
        "     <g id=\"line2d_16\">\n",
        "      <path d=\"M 51.262547 11.588098 \n",
@@ -2075,6 +3028,7 @@
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        "      <!-- 0.9 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(23.394438 15.976195) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <defs>\n",
        "        <path id=\"DejaVuSans-39\" d=\"M 703 97 \n",
        "L 703 672 \n",
@@ -2113,9 +3067,15 @@
        "      </g>\n",
        "     </g>\n",
        "    </g>\n",
+<<<<<<< HEAD
+       "    <g id=\"text_15\">\n",
+       "     <!-- metametric -->\n",
+       "     <g style=\"fill: #262626\" transform=\"translate(16.774031 182.430563) rotate(-90) scale(0.126 -0.126)\">\n",
+=======
        "    <g id=\"text_13\">\n",
        "     <!-- metametric -->\n",
        "     <g style=\"fill: #262626\" transform=\"translate(16.774031 184.887528) rotate(-90) scale(0.126 -0.126)\">\n",
+>>>>>>> dev
        "      <defs>\n",
        "       <path id=\"DejaVuSans-6d\" d=\"M 3328 2828 \n",
        "Q 3544 3216 3844 3400 \n",
@@ -2179,6 +3139,257 @@
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        "   <g id=\"patch_3\">\n",
+<<<<<<< HEAD
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+       "z\n",
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+=======
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@@ -2361,11 +3572,16 @@
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        "    <path d=\"M 51.262547 286.856966 \n",
        "L 609.262547 286.856966 \n",
+>>>>>>> dev
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        "    <defs>\n",
+<<<<<<< HEAD
+       "     <path id=\"mfa61d6325d\" d=\"M 0 2.5 \n",
+=======
        "     <path id=\"m9b44bc897f\" d=\"M 0 2.5 \n",
+>>>>>>> dev
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@@ -2377,6 +3593,630 @@
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+       "     <use xlink:href=\"#mfa61d6325d\" x=\"558.152071\" y=\"68.710846\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"562.052933\" y=\"46.544922\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"562.173481\" y=\"169.523977\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"566.773279\" y=\"128.353033\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"559.969297\" y=\"177.52312\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"567.617877\" y=\"86.380958\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"559.296912\" y=\"68.391436\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"566.947267\" y=\"21.559066\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"562.35899\" y=\"185.550442\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"556.8868\" y=\"142.234165\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"563.090999\" y=\"48.42558\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"556.364335\" y=\"74.220411\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"562.338058\" y=\"84.042552\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"568.891268\" y=\"76.598134\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"570.991972\" y=\"128.565752\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"569.500487\" y=\"155.924658\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"563.455218\" y=\"67.388921\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"571.439394\" y=\"44.958641\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"566.144814\" y=\"173.347799\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"563.316688\" y=\"87.888627\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"557.419302\" y=\"98.719282\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"559.195976\" y=\"75.616422\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"565.776479\" y=\"91.329723\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"554.981278\" y=\"132.096154\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"564.709396\" y=\"55.359778\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"562.628421\" y=\"96.385745\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"554.660757\" y=\"84.000333\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"565.922597\" y=\"51.63436\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"564.264776\" y=\"49.324895\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"568.808958\" y=\"78.55165\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"557.877455\" y=\"84.137637\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"565.273795\" y=\"172.731404\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"558.364971\" y=\"59.500576\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"560.746553\" y=\"116.891008\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"560.623442\" y=\"84.340692\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"559.2942\" y=\"79.929883\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"561.766514\" y=\"79.684683\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"557.961827\" y=\"149.164428\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"566.638711\" y=\"152.871748\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"569.747097\" y=\"114.217086\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"561.564424\" y=\"69.69846\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"572.031613\" y=\"63.721829\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"561.07472\" y=\"64.386221\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"560.682793\" y=\"69.315856\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"556.185378\" y=\"68.933941\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"562.440675\" y=\"167.360275\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"559.678721\" y=\"169.509002\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"553.630676\" y=\"67.245224\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"565.216579\" y=\"79.362051\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"553.878613\" y=\"69.622947\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"570.309533\" y=\"76.756143\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"568.641939\" y=\"80.958807\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"557.923005\" y=\"176.270536\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"554.852006\" y=\"86.588955\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"561.976936\" y=\"66.198006\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"562.684762\" y=\"99.910591\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"565.095199\" y=\"141.222749\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"566.453702\" y=\"157.688077\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"560.553621\" y=\"54.748227\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"567.581745\" y=\"164.638921\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"563.791595\" y=\"139.190499\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"559.942852\" y=\"88.559339\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"566.890317\" y=\"78.397355\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"561.486101\" y=\"83.746181\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"559.590242\" y=\"40.114625\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "     <use xlink:href=\"#mfa61d6325d\" x=\"559.266129\" y=\"124.996252\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
+       "    </g>\n",
+=======
        "    <g clip-path=\"url(#p63f3bb8d7f)\">\n",
        "     <use xlink:href=\"#m9b44bc897f\" x=\"107.062547\" y=\"98.089295\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
        "     <use xlink:href=\"#m9b44bc897f\" x=\"92.605252\" y=\"50.541727\" style=\"fill: #4c4c4c; fill-opacity: 0.7\"/>\n",
@@ -3812,18 +5652,28 @@
        "    <path d=\"M 58.611234 230.53275 \n",
        "L 468.885234 230.53275 \n",
        "\" style=\"fill: none; stroke: #262626; stroke-width: 1.25; stroke-linejoin: miter; stroke-linecap: square\"/>\n",
+>>>>>>> dev
        "   </g>\n",
        "  </g>\n",
        " </g>\n",
        " <defs>\n",
+<<<<<<< HEAD
+       "  <clipPath id=\"pbd6c032448\">\n",
+       "   <rect x=\"51.262547\" y=\"7.2\" width=\"558\" height=\"277.2\"/>\n",
+=======
        "  <clipPath id=\"pffe368b823\">\n",
        "   <rect x=\"58.611234\" y=\"7.2\" width=\"410.274\" height=\"223.33275\"/>\n",
+>>>>>>> dev
        "  </clipPath>\n",
        " </defs>\n",
        "</svg>\n"
       ],
       "text/plain": [
+<<<<<<< HEAD
+       "<Figure size 1000x500 with 1 Axes>"
+=======
        "<Figure size 680x400 with 1 Axes>"
+>>>>>>> dev
       ]
      },
      "metadata": {
@@ -3833,17 +5683,51 @@
     }
    ],
    "source": [
+<<<<<<< HEAD
+    "pal = sns.color_palette(\"light:b_r\", as_cmap=False)\n",
+    "#pal = sns.light_palette(\"seagreen\", reverse=True, as_cmap=False)\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 1, figsize=(10, 5))\n",
+    "\n",
+    "sns.boxplot(\n",
+    "    data=df,\n",
+    "    x=\"guidance_density\",\n",
+    "    y=\"metametric\",\n",
+    "    order=ORDER,\n",
+    "    palette=pal,\n",
+    ")\n",
+    "\n",
+    "sns.stripplot(\n",
+    "    data=df,\n",
+    "    x=\"guidance_density\",\n",
+    "    y=\"metametric\",\n",
+    "    order=ORDER,\n",
+    "    size=5, color=\".3\", alpha=0.7,\n",
+    ")\n",
+    "sns.despine()\n",
+    "axs.grid(True, axis='y', alpha=0.5)\n",
+    "fig.savefig(\n",
+    "    \"examples/eval/figures_data/wosac/log_replay_guidance_density.pdf\",\n",
+    "    dpi=300,\n",
+    "    bbox_inches=\"tight\",\n",
+    ")"
+=======
     "g = sns.catplot(\n",
     "    data=df, x=\"guidance_density\", y=\"metametric\", capsize=.2, palette=pal, errorbar=\"sd\",\n",
     "    kind=\"point\", height=4, aspect=1.7, order=[1.0, 0.8, 0.5, 0.2, 0.01],\n",
     ")\n",
     "g.despine(left=True)\n",
     "g.ax.grid(True, axis='y', alpha=0.3)"
+>>>>>>> dev
    ]
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 68,
+=======
    "execution_count": 22,
+>>>>>>> dev
    "id": "e439cbc6",
    "metadata": {},
    "outputs": [
@@ -3858,7 +5742,11 @@
        "  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n",
        "   <cc:Work>\n",
        "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
+<<<<<<< HEAD
+       "    <dc:date>2025-05-14T22:14:36.912047</dc:date>\n",
+=======
        "    <dc:date>2025-05-14T17:14:45.000413</dc:date>\n",
+>>>>>>> dev
        "    <dc:format>image/svg+xml</dc:format>\n",
        "    <dc:creator>\n",
        "     <cc:Agent>\n",
@@ -3894,6 +5782,15 @@
        "   <g id=\"matplotlib.axis_1\">\n",
        "    <g id=\"xtick_1\">\n",
        "     <g id=\"line2d_1\">\n",
+<<<<<<< HEAD
+       "      <path d=\"M 97.762547 284.4 \n",
+       "L 97.762547 7.2 \n",
+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
+       "     </g>\n",
+       "     <g id=\"line2d_2\">\n",
+       "      <defs>\n",
+       "       <path id=\"m9091dd3b43\" d=\"M 0 0 \n",
+=======
        "      <path d=\"M 107.062547 284.4 \n",
        "L 107.062547 7.2 \n",
        "\" clip-path=\"url(#p3cee712e73)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
@@ -3901,16 +5798,25 @@
        "     <g id=\"line2d_2\">\n",
        "      <defs>\n",
        "       <path id=\"m0e71b34faf\" d=\"M 0 0 \n",
+>>>>>>> dev
        "L 0 6 \n",
        "\" style=\"stroke: #262626; stroke-width: 1.25\"/>\n",
        "      </defs>\n",
        "      <g>\n",
+<<<<<<< HEAD
+       "       <use xlink:href=\"#m9091dd3b43\" x=\"97.762547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+=======
        "       <use xlink:href=\"#m0e71b34faf\" x=\"107.062547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+>>>>>>> dev
        "      </g>\n",
        "     </g>\n",
        "     <g id=\"text_1\">\n",
        "      <!-- 1.0 -->\n",
+<<<<<<< HEAD
+       "      <g style=\"fill: #262626\" transform=\"translate(88.578492 302.676195) scale(0.1155 -0.1155)\">\n",
+=======
        "      <g style=\"fill: #262626\" transform=\"translate(97.878492 302.676195) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <defs>\n",
        "        <path id=\"DejaVuSans-31\" d=\"M 794 531 \n",
        "L 1825 531 \n",
@@ -3963,6 +5869,31 @@
        "    </g>\n",
        "    <g id=\"xtick_2\">\n",
        "     <g id=\"line2d_3\">\n",
+<<<<<<< HEAD
+       "      <path d=\"M 190.762547 284.4 \n",
+       "L 190.762547 7.2 \n",
+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
+       "     </g>\n",
+       "     <g id=\"line2d_4\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m9091dd3b43\" x=\"190.762547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_2\">\n",
+       "      <!-- 0.75 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(177.904148 302.676195) scale(0.1155 -0.1155)\">\n",
+       "       <defs>\n",
+       "        <path id=\"DejaVuSans-37\" d=\"M 525 4666 \n",
+       "L 3525 4666 \n",
+       "L 3525 4397 \n",
+       "L 1831 0 \n",
+       "L 1172 0 \n",
+       "L 2766 4134 \n",
+       "L 525 4134 \n",
+       "L 525 4666 \n",
+       "z\n",
+       "\" transform=\"scale(0.015625)\"/>\n",
+=======
        "      <path d=\"M 218.662547 284.4 \n",
        "L 218.662547 7.2 \n",
        "\" clip-path=\"url(#p3cee712e73)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
@@ -4037,6 +5968,7 @@
        "      <!-- 0.5 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(321.078492 302.676195) scale(0.1155 -0.1155)\">\n",
        "       <defs>\n",
+>>>>>>> dev
        "        <path id=\"DejaVuSans-35\" d=\"M 691 4666 \n",
        "L 3169 4666 \n",
        "L 3169 4134 \n",
@@ -4065,12 +5997,50 @@
        "       </defs>\n",
        "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
+<<<<<<< HEAD
+       "       <use xlink:href=\"#DejaVuSans-37\" x=\"95.410156\"/>\n",
+       "       <use xlink:href=\"#DejaVuSans-35\" x=\"159.033203\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "    </g>\n",
+       "    <g id=\"xtick_3\">\n",
+       "     <g id=\"line2d_5\">\n",
+       "      <path d=\"M 283.762547 284.4 \n",
+       "L 283.762547 7.2 \n",
+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
+       "     </g>\n",
+       "     <g id=\"line2d_6\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m9091dd3b43\" x=\"283.762547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_3\">\n",
+       "      <!-- 0.5 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(274.578492 302.676195) scale(0.1155 -0.1155)\">\n",
+       "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
+       "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
+=======
+>>>>>>> dev
        "       <use xlink:href=\"#DejaVuSans-35\" x=\"95.410156\"/>\n",
        "      </g>\n",
        "     </g>\n",
        "    </g>\n",
        "    <g id=\"xtick_4\">\n",
        "     <g id=\"line2d_7\">\n",
+<<<<<<< HEAD
+       "      <path d=\"M 376.762547 284.4 \n",
+       "L 376.762547 7.2 \n",
+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
+       "     </g>\n",
+       "     <g id=\"line2d_8\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m9091dd3b43\" x=\"376.762547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_4\">\n",
+       "      <!-- 0.25 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(363.904148 302.676195) scale(0.1155 -0.1155)\">\n",
+=======
        "      <path d=\"M 441.862547 284.4 \n",
        "L 441.862547 7.2 \n",
        "\" clip-path=\"url(#p3cee712e73)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
@@ -4083,6 +6053,7 @@
        "     <g id=\"text_4\">\n",
        "      <!-- 0.2 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(432.678492 302.676195) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <defs>\n",
        "        <path id=\"DejaVuSans-32\" d=\"M 1228 531 \n",
        "L 3431 531 \n",
@@ -4112,11 +6083,24 @@
        "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-32\" x=\"95.410156\"/>\n",
+<<<<<<< HEAD
+       "       <use xlink:href=\"#DejaVuSans-35\" x=\"159.033203\"/>\n",
+=======
+>>>>>>> dev
        "      </g>\n",
        "     </g>\n",
        "    </g>\n",
        "    <g id=\"xtick_5\">\n",
        "     <g id=\"line2d_9\">\n",
+<<<<<<< HEAD
+       "      <path d=\"M 469.762547 284.4 \n",
+       "L 469.762547 7.2 \n",
+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
+       "     </g>\n",
+       "     <g id=\"line2d_10\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m9091dd3b43\" x=\"469.762547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+=======
        "      <path d=\"M 553.462547 284.4 \n",
        "L 553.462547 7.2 \n",
        "\" clip-path=\"url(#p3cee712e73)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
@@ -4124,11 +6108,16 @@
        "     <g id=\"line2d_10\">\n",
        "      <g>\n",
        "       <use xlink:href=\"#m0e71b34faf\" x=\"553.462547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+>>>>>>> dev
        "      </g>\n",
        "     </g>\n",
        "     <g id=\"text_5\">\n",
        "      <!-- 0.01 -->\n",
+<<<<<<< HEAD
+       "      <g style=\"fill: #262626\" transform=\"translate(456.904148 302.676195) scale(0.1155 -0.1155)\">\n",
+=======
        "      <g style=\"fill: #262626\" transform=\"translate(540.604148 302.676195) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-30\" x=\"95.410156\"/>\n",
@@ -4136,7 +6125,31 @@
        "      </g>\n",
        "     </g>\n",
        "    </g>\n",
+<<<<<<< HEAD
+       "    <g id=\"xtick_6\">\n",
+       "     <g id=\"line2d_11\">\n",
+       "      <path d=\"M 562.762547 284.4 \n",
+       "L 562.762547 7.2 \n",
+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
+       "     </g>\n",
+       "     <g id=\"line2d_12\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m9091dd3b43\" x=\"562.762547\" y=\"284.4\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_6\">\n",
+       "      <!-- 0.0 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(553.578492 302.676195) scale(0.1155 -0.1155)\">\n",
+       "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
+       "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
+       "       <use xlink:href=\"#DejaVuSans-30\" x=\"95.410156\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "    </g>\n",
+       "    <g id=\"text_7\">\n",
+=======
        "    <g id=\"text_6\">\n",
+>>>>>>> dev
        "     <!-- guidance_density -->\n",
        "     <g style=\"fill: #262626\" transform=\"translate(275.0775 318.652266) scale(0.126 -0.126)\">\n",
        "      <defs>\n",
@@ -4431,6 +6444,16 @@
        "   </g>\n",
        "   <g id=\"matplotlib.axis_2\">\n",
        "    <g id=\"ytick_1\">\n",
+<<<<<<< HEAD
+       "     <g id=\"line2d_13\">\n",
+       "      <path d=\"M 51.262547 268.195488 \n",
+       "L 609.262547 268.195488 \n",
+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
+       "     </g>\n",
+       "     <g id=\"line2d_14\">\n",
+       "      <defs>\n",
+       "       <path id=\"m443bfe116e\" d=\"M 0 0 \n",
+=======
        "     <g id=\"line2d_11\">\n",
        "      <path d=\"M 51.262547 283.856115 \n",
        "L 609.262547 283.856115 \n",
@@ -4439,16 +6462,26 @@
        "     <g id=\"line2d_12\">\n",
        "      <defs>\n",
        "       <path id=\"m156ba10228\" d=\"M 0 0 \n",
+>>>>>>> dev
        "L -6 0 \n",
        "\" style=\"stroke: #262626; stroke-width: 1.25\"/>\n",
        "      </defs>\n",
        "      <g>\n",
+<<<<<<< HEAD
+       "       <use xlink:href=\"#m443bfe116e\" x=\"51.262547\" y=\"268.195488\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_8\">\n",
+       "      <!-- 0.2 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 272.583586) scale(0.1155 -0.1155)\">\n",
+=======
        "       <use xlink:href=\"#m156ba10228\" x=\"51.262547\" y=\"283.856115\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
        "      </g>\n",
        "     </g>\n",
        "     <g id=\"text_7\">\n",
        "      <!-- 0.2 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(23.394438 288.244213) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-32\" x=\"95.410156\"/>\n",
@@ -4456,6 +6489,21 @@
        "     </g>\n",
        "    </g>\n",
        "    <g id=\"ytick_2\">\n",
+<<<<<<< HEAD
+       "     <g id=\"line2d_15\">\n",
+       "      <path d=\"M 51.262547 237.070448 \n",
+       "L 609.262547 237.070448 \n",
+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
+       "     </g>\n",
+       "     <g id=\"line2d_16\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m443bfe116e\" x=\"51.262547\" y=\"237.070448\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_9\">\n",
+       "      <!-- 0.3 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 241.458546) scale(0.1155 -0.1155)\">\n",
+=======
        "     <g id=\"line2d_13\">\n",
        "      <path d=\"M 51.262547 250.154005 \n",
        "L 609.262547 250.154005 \n",
@@ -4469,6 +6517,7 @@
        "     <g id=\"text_8\">\n",
        "      <!-- 0.3 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(23.394438 254.542103) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <defs>\n",
        "        <path id=\"DejaVuSans-33\" d=\"M 2597 2516 \n",
        "Q 3050 2419 3304 2112 \n",
@@ -4510,6 +6559,21 @@
        "     </g>\n",
        "    </g>\n",
        "    <g id=\"ytick_3\">\n",
+<<<<<<< HEAD
+       "     <g id=\"line2d_17\">\n",
+       "      <path d=\"M 51.262547 205.945408 \n",
+       "L 609.262547 205.945408 \n",
+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
+       "     </g>\n",
+       "     <g id=\"line2d_18\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m443bfe116e\" x=\"51.262547\" y=\"205.945408\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_10\">\n",
+       "      <!-- 0.4 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 210.333506) scale(0.1155 -0.1155)\">\n",
+=======
        "     <g id=\"line2d_15\">\n",
        "      <path d=\"M 51.262547 216.451895 \n",
        "L 609.262547 216.451895 \n",
@@ -4523,6 +6587,7 @@
        "     <g id=\"text_9\">\n",
        "      <!-- 0.4 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(23.394438 220.839992) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <defs>\n",
        "        <path id=\"DejaVuSans-34\" d=\"M 2419 4116 \n",
        "L 825 1625 \n",
@@ -4551,6 +6616,21 @@
        "     </g>\n",
        "    </g>\n",
        "    <g id=\"ytick_4\">\n",
+<<<<<<< HEAD
+       "     <g id=\"line2d_19\">\n",
+       "      <path d=\"M 51.262547 174.820368 \n",
+       "L 609.262547 174.820368 \n",
+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
+       "     </g>\n",
+       "     <g id=\"line2d_20\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m443bfe116e\" x=\"51.262547\" y=\"174.820368\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_11\">\n",
+       "      <!-- 0.5 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 179.208466) scale(0.1155 -0.1155)\">\n",
+=======
        "     <g id=\"line2d_17\">\n",
        "      <path d=\"M 51.262547 182.749784 \n",
        "L 609.262547 182.749784 \n",
@@ -4564,6 +6644,7 @@
        "     <g id=\"text_10\">\n",
        "      <!-- 0.5 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(23.394438 187.137882) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-35\" x=\"95.410156\"/>\n",
@@ -4571,6 +6652,21 @@
        "     </g>\n",
        "    </g>\n",
        "    <g id=\"ytick_5\">\n",
+<<<<<<< HEAD
+       "     <g id=\"line2d_21\">\n",
+       "      <path d=\"M 51.262547 143.695328 \n",
+       "L 609.262547 143.695328 \n",
+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
+       "     </g>\n",
+       "     <g id=\"line2d_22\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m443bfe116e\" x=\"51.262547\" y=\"143.695328\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_12\">\n",
+       "      <!-- 0.6 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 148.083426) scale(0.1155 -0.1155)\">\n",
+=======
        "     <g id=\"line2d_19\">\n",
        "      <path d=\"M 51.262547 149.047674 \n",
        "L 609.262547 149.047674 \n",
@@ -4584,6 +6680,7 @@
        "     <g id=\"text_11\">\n",
        "      <!-- 0.6 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(23.394438 153.435772) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <defs>\n",
        "        <path id=\"DejaVuSans-36\" d=\"M 2113 2584 \n",
        "Q 1688 2584 1439 2293 \n",
@@ -4623,6 +6720,21 @@
        "     </g>\n",
        "    </g>\n",
        "    <g id=\"ytick_6\">\n",
+<<<<<<< HEAD
+       "     <g id=\"line2d_23\">\n",
+       "      <path d=\"M 51.262547 112.570288 \n",
+       "L 609.262547 112.570288 \n",
+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
+       "     </g>\n",
+       "     <g id=\"line2d_24\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m443bfe116e\" x=\"51.262547\" y=\"112.570288\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_13\">\n",
+       "      <!-- 0.7 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 116.958386) scale(0.1155 -0.1155)\">\n",
+=======
        "     <g id=\"line2d_21\">\n",
        "      <path d=\"M 51.262547 115.345564 \n",
        "L 609.262547 115.345564 \n",
@@ -4648,6 +6760,7 @@
        "z\n",
        "\" transform=\"scale(0.015625)\"/>\n",
        "       </defs>\n",
+>>>>>>> dev
        "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-37\" x=\"95.410156\"/>\n",
@@ -4655,6 +6768,62 @@
        "     </g>\n",
        "    </g>\n",
        "    <g id=\"ytick_7\">\n",
+<<<<<<< HEAD
+       "     <g id=\"line2d_25\">\n",
+       "      <path d=\"M 51.262547 81.445248 \n",
+       "L 609.262547 81.445248 \n",
+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
+       "     </g>\n",
+       "     <g id=\"line2d_26\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m443bfe116e\" x=\"51.262547\" y=\"81.445248\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_14\">\n",
+       "      <!-- 0.8 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 85.833346) scale(0.1155 -0.1155)\">\n",
+       "       <defs>\n",
+       "        <path id=\"DejaVuSans-38\" d=\"M 2034 2216 \n",
+       "Q 1584 2216 1326 1975 \n",
+       "Q 1069 1734 1069 1313 \n",
+       "Q 1069 891 1326 650 \n",
+       "Q 1584 409 2034 409 \n",
+       "Q 2484 409 2743 651 \n",
+       "Q 3003 894 3003 1313 \n",
+       "Q 3003 1734 2745 1975 \n",
+       "Q 2488 2216 2034 2216 \n",
+       "z\n",
+       "M 1403 2484 \n",
+       "Q 997 2584 770 2862 \n",
+       "Q 544 3141 544 3541 \n",
+       "Q 544 4100 942 4425 \n",
+       "Q 1341 4750 2034 4750 \n",
+       "Q 2731 4750 3128 4425 \n",
+       "Q 3525 4100 3525 3541 \n",
+       "Q 3525 3141 3298 2862 \n",
+       "Q 3072 2584 2669 2484 \n",
+       "Q 3125 2378 3379 2068 \n",
+       "Q 3634 1759 3634 1313 \n",
+       "Q 3634 634 3220 271 \n",
+       "Q 2806 -91 2034 -91 \n",
+       "Q 1263 -91 848 271 \n",
+       "Q 434 634 434 1313 \n",
+       "Q 434 1759 690 2068 \n",
+       "Q 947 2378 1403 2484 \n",
+       "z\n",
+       "M 1172 3481 \n",
+       "Q 1172 3119 1398 2916 \n",
+       "Q 1625 2713 2034 2713 \n",
+       "Q 2441 2713 2670 2916 \n",
+       "Q 2900 3119 2900 3481 \n",
+       "Q 2900 3844 2670 4047 \n",
+       "Q 2441 4250 2034 4250 \n",
+       "Q 1625 4250 1398 4047 \n",
+       "Q 1172 3844 1172 3481 \n",
+       "z\n",
+       "\" transform=\"scale(0.015625)\"/>\n",
+       "       </defs>\n",
+=======
        "     <g id=\"line2d_23\">\n",
        "      <path d=\"M 51.262547 81.643453 \n",
        "L 609.262547 81.643453 \n",
@@ -4668,6 +6837,7 @@
        "     <g id=\"text_13\">\n",
        "      <!-- 0.8 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(23.394438 86.031551) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <use xlink:href=\"#DejaVuSans-30\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-2e\" x=\"63.623047\"/>\n",
        "       <use xlink:href=\"#DejaVuSans-38\" x=\"95.410156\"/>\n",
@@ -4675,6 +6845,21 @@
        "     </g>\n",
        "    </g>\n",
        "    <g id=\"ytick_8\">\n",
+<<<<<<< HEAD
+       "     <g id=\"line2d_27\">\n",
+       "      <path d=\"M 51.262547 50.320208 \n",
+       "L 609.262547 50.320208 \n",
+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
+       "     </g>\n",
+       "     <g id=\"line2d_28\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m443bfe116e\" x=\"51.262547\" y=\"50.320208\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_15\">\n",
+       "      <!-- 0.9 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 54.708306) scale(0.1155 -0.1155)\">\n",
+=======
        "     <g id=\"line2d_25\">\n",
        "      <path d=\"M 51.262547 47.941343 \n",
        "L 609.262547 47.941343 \n",
@@ -4688,6 +6873,7 @@
        "     <g id=\"text_14\">\n",
        "      <!-- 0.9 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(23.394438 52.32944) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
        "       <defs>\n",
        "        <path id=\"DejaVuSans-39\" d=\"M 703 97 \n",
        "L 703 672 \n",
@@ -4727,6 +6913,21 @@
        "     </g>\n",
        "    </g>\n",
        "    <g id=\"ytick_9\">\n",
+<<<<<<< HEAD
+       "     <g id=\"line2d_29\">\n",
+       "      <path d=\"M 51.262547 19.195168 \n",
+       "L 609.262547 19.195168 \n",
+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #cccccc; stroke-opacity: 0.5; stroke-linecap: round\"/>\n",
+       "     </g>\n",
+       "     <g id=\"line2d_30\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m443bfe116e\" x=\"51.262547\" y=\"19.195168\" style=\"fill: #262626; stroke: #262626; stroke-width: 1.25\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "     <g id=\"text_16\">\n",
+       "      <!-- 1.0 -->\n",
+       "      <g style=\"fill: #262626\" transform=\"translate(23.394438 23.583266) scale(0.1155 -0.1155)\">\n",
+=======
        "     <g id=\"line2d_27\">\n",
        "      <path d=\"M 51.262547 14.239232 \n",
        "L 609.262547 14.239232 \n",
@@ -4740,13 +6941,18 @@
        "     <g id=\"text_15\">\n",
        "      <!-- 1.0 -->\n",
        "      <g style=\"fill: #262626\" transform=\"translate(23.394438 18.62733) scale(0.1155 -0.1155)\">\n",
+>>>>>>> dev
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        "     </g>\n",
        "    </g>\n",
+<<<<<<< HEAD
+       "    <g id=\"text_17\">\n",
+=======
        "    <g id=\"text_16\">\n",
+>>>>>>> dev
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+=======
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+>>>>>>> dev
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+<<<<<<< HEAD
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+>>>>>>> dev
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+>>>>>>> dev
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+       "L 562.926605 -114.992063 \n",
+       "L 562.881835 -113.068941 \n",
+       "z\n",
+       "\" style=\"stroke: #4c4c4c; stroke-width: 1.25\"/>\n",
+       "    </defs>\n",
+       "    <g clip-path=\"url(#p2c5065e546)\">\n",
+       "     <use xlink:href=\"#mdc47eb14b0\" x=\"0\" y=\"328.823109\" style=\"fill: #5875a4; stroke: #4c4c4c; stroke-width: 1.25\"/>\n",
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+       "   </g>\n",
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+       "   </g>\n",
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+       "    <defs>\n",
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+       "\" style=\"stroke: #ffffff; stroke-width: 1.125\"/>\n",
+       "    </defs>\n",
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+       "   <g id=\"line2d_34\">\n",
+       "    <path d=\"M 190.762547 213.989021 \n",
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+       "    <path d=\"M 283.762547 141.400247 \n",
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+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #4c4c4c; stroke-width: 5.625; stroke-linecap: round\"/>\n",
+       "   </g>\n",
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+       "    <path d=\"M 376.762547 131.94025 \n",
+       "L 376.762547 86.202971 \n",
+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #4c4c4c; stroke-width: 5.625; stroke-linecap: round\"/>\n",
+       "   </g>\n",
+       "   <g id=\"line2d_42\">\n",
+       "    <path d=\"M 376.762547 94.620733 \n",
+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #4c4c4c; stroke-width: 1.5; stroke-linecap: round\"/>\n",
+       "    <g clip-path=\"url(#p2c5065e546)\">\n",
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+       "    </g>\n",
+       "   </g>\n",
+       "   <g id=\"line2d_43\">\n",
+       "    <path d=\"M 469.762547 187.358299 \n",
+       "L 469.762547 50.382198 \n",
+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #4c4c4c; stroke-width: 1.875; stroke-linecap: round\"/>\n",
+       "   </g>\n",
+       "   <g id=\"line2d_44\">\n",
+       "    <path d=\"M 469.762547 137.45602 \n",
+       "L 469.762547 90.456511 \n",
+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #4c4c4c; stroke-width: 5.625; stroke-linecap: round\"/>\n",
+       "   </g>\n",
+       "   <g id=\"line2d_45\">\n",
+       "    <path d=\"M 469.762547 100.205506 \n",
+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #4c4c4c; stroke-width: 1.5; stroke-linecap: round\"/>\n",
+       "    <g clip-path=\"url(#p2c5065e546)\">\n",
+       "     <use xlink:href=\"#mb0a1f8e2e7\" x=\"469.762547\" y=\"100.205506\" style=\"fill: #ffffff; stroke: #ffffff; stroke-width: 1.125\"/>\n",
+       "    </g>\n",
+       "   </g>\n",
+       "   <g id=\"line2d_46\">\n",
+       "    <path d=\"M 562.762547 189.409897 \n",
+       "L 562.762547 51.709387 \n",
+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #4c4c4c; stroke-width: 1.875; stroke-linecap: round\"/>\n",
+       "   </g>\n",
+       "   <g id=\"line2d_47\">\n",
+       "    <path d=\"M 562.762547 139.289056 \n",
+       "L 562.762547 87.410941 \n",
+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #4c4c4c; stroke-width: 5.625; stroke-linecap: round\"/>\n",
+       "   </g>\n",
+       "   <g id=\"line2d_48\">\n",
+       "    <path d=\"M 562.762547 98.836315 \n",
+       "\" clip-path=\"url(#p2c5065e546)\" style=\"fill: none; stroke: #4c4c4c; stroke-width: 1.5; stroke-linecap: round\"/>\n",
+       "    <g clip-path=\"url(#p2c5065e546)\">\n",
+       "     <use xlink:href=\"#mb0a1f8e2e7\" x=\"562.762547\" y=\"98.836315\" style=\"fill: #ffffff; stroke: #ffffff; stroke-width: 1.125\"/>\n",
+=======
        "     <path id=\"m267d1c2bdd\" d=\"M 553.595805 -76.133985 \n",
        "L 553.329289 -76.133985 \n",
        "L 553.288923 -78.450843 \n",
@@ -5954,6 +9527,7 @@
        "\" clip-path=\"url(#p3cee712e73)\" style=\"fill: none; stroke: #4c4c4c; stroke-width: 1.5; stroke-linecap: round\"/>\n",
        "    <g clip-path=\"url(#p3cee712e73)\">\n",
        "     <use xlink:href=\"#m8e9d3ed8d4\" x=\"553.462547\" y=\"96.693938\" style=\"fill: #ffffff; stroke: #ffffff; stroke-width: 1.125\"/>\n",
+>>>>>>> dev
        "    </g>\n",
        "   </g>\n",
        "   <g id=\"patch_3\">\n",
@@ -5969,7 +9543,11 @@
        "  </g>\n",
        " </g>\n",
        " <defs>\n",
+<<<<<<< HEAD
+       "  <clipPath id=\"p2c5065e546\">\n",
+=======
        "  <clipPath id=\"p3cee712e73\">\n",
+>>>>>>> dev
        "   <rect x=\"51.262547\" y=\"7.2\" width=\"558\" height=\"277.2\"/>\n",
        "  </clipPath>\n",
        " </defs>\n",
@@ -5992,7 +9570,11 @@
     "    data=df,\n",
     "    x=\"guidance_density\",\n",
     "    y=\"metametric\",\n",
+<<<<<<< HEAD
+    "    order=ORDER,\n",
+=======
     "    order=[1.0, 0.8, 0.5, 0.2, 0.01],\n",
+>>>>>>> dev
     ")\n",
     "\n",
     "sns.despine()\n",
diff --git a/examples/eval/run_wosac_eval.py b/examples/eval/run_wosac_eval.py
index 17b999ef4..f18046600 100644
--- a/examples/eval/run_wosac_eval.py
+++ b/examples/eval/run_wosac_eval.py
@@ -71,6 +71,7 @@ def rollout(
     video_dir: str = "videos",
     video_format: str = "gif",
     guidance_mode: str = "vbd_online",
+    guidance_mode: str = "vbd_online",
 ):
     """Rollout agent in the environment and return the scenario rollouts."""
     # Storage
@@ -267,32 +268,32 @@ def load_config(config_path):
     MAX_AGENTS = (
         madrona_gpudrive.kMaxAgentCount
     )  # TODO: Set to 128 for real eval
-    NUM_ENVS = 100
+    NUM_ENVS = 10
     DEVICE = "cuda"  # where to run the env rollouts
     NUM_ROLLOUTS_PER_BATCH = 1
-    NUM_DATA_BATCHES = 1
+    NUM_DATA_BATCHES = 5
     INIT_STEPS = 10
-    DATASET_SIZE = 100
+    DATASET_SIZE = 1000
     RENDER = False
     LOG_DIR = "examples/eval/figures_data/wosac/"
     GUIDANCE_MODE = (
-        "log_replay"  # Options: "vbd_amortized", "vbd_online", "log_replay"
+        "vbd_online"  # Options: "vbd_amortized", "vbd_online", "log_replay"
     )
     GUIDANCE_DROPOUT_MODE = "avg"  # Options: "max", "avg", "remove_all"
-    GUIDANCE_DROPOUT_PROB = 0.0
+    GUIDANCE_DROPOUT_PROB = 0.99
     SMOOTHEN_TRAJECTORY = True
 
-    DATA_JSON = "data/processed/wosac/validation/json"
-    DATA_TFRECORD = "data/processed/wosac/validation/tfrecord"
+    DATA_JSON = "data/processed/wosac/validation_interactive/json"
+    DATA_TFRECORD = "data/processed/wosac/validation_interactive/tfrecord"
 
-    CPT_PATH = "checkpoints/model_guidance_logs__R_10000__05_14_16_54_46_975_000300.pt"
+    CPT_PATH = "checkpoints/model_guidance_logs__R_10000__05_14_16_54_46_975_002200.pt"
 
     # Create data loader
     val_loader = SceneDataLoader(
         root=DATA_JSON,
         batch_size=NUM_ENVS,
         dataset_size=DATASET_SIZE,
-        sample_with_replacement=True,
+        sample_with_replacement=False,
         shuffle=True,
         file_prefix="",
         seed=10,
diff --git a/gpudrive/env/env_torch.py b/gpudrive/env/env_torch.py
index c4c41bf2c..6d7decab2 100755
--- a/gpudrive/env/env_torch.py
+++ b/gpudrive/env/env_torch.py
@@ -1753,6 +1753,8 @@ def swap_data_batch(self, data_batch=None):
             controlled_agent_mask=self.cont_agent_mask,
             reference_trajectory=self.reference_trajectory,
         )
+        
+        self.guidance_dropout_mask = self.create_guidance_dropout_mask()
 
     def get_expert_actions(self):
         """Get expert actions for the full trajectories across worlds.
diff --git a/src/consts.hpp b/src/consts.hpp
index 604c9838c..edaa5f46b 100644
--- a/src/consts.hpp
+++ b/src/consts.hpp
@@ -22,7 +22,7 @@ inline constexpr float worldLength = 40.f;
 // This factor rescales the length of the vehicles by a tiny amount
 // To account for the fact that noise occasionally puts vehicles into initial
 // collisions. This is a dataset artifact that we are handling here like this.
-inline constexpr float vehicleLengthScale = 0.9f;
+inline constexpr float vehicleLengthScale = 1.0f;
 
 // Each unit of distance forward (+ y axis) rewards the agents by this amount
 inline constexpr float rewardPerDist = 0.05f;

From 58620b196cef27a8f0c0ffba8e8e706486631eb0 Mon Sep 17 00:00:00 2001
From: Daphne Cornelisse <cor.daphne@gmail.com>
Date: Thu, 15 May 2025 15:12:19 -0400
Subject: [PATCH 19/38] Small fix

---
 examples/eval/run_wosac_eval.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/examples/eval/run_wosac_eval.py b/examples/eval/run_wosac_eval.py
index f18046600..b801b78ec 100644
--- a/examples/eval/run_wosac_eval.py
+++ b/examples/eval/run_wosac_eval.py
@@ -71,7 +71,6 @@ def rollout(
     video_dir: str = "videos",
     video_format: str = "gif",
     guidance_mode: str = "vbd_online",
-    guidance_mode: str = "vbd_online",
 ):
     """Rollout agent in the environment and return the scenario rollouts."""
     # Storage
@@ -268,10 +267,10 @@ def load_config(config_path):
     MAX_AGENTS = (
         madrona_gpudrive.kMaxAgentCount
     )  # TODO: Set to 128 for real eval
-    NUM_ENVS = 10
+    NUM_ENVS = 200
     DEVICE = "cuda"  # where to run the env rollouts
     NUM_ROLLOUTS_PER_BATCH = 1
-    NUM_DATA_BATCHES = 5
+    NUM_DATA_BATCHES = 1
     INIT_STEPS = 10
     DATASET_SIZE = 1000
     RENDER = False

From 2cffa6ab703a9f3240bc9d8de3cf2c77c52de068 Mon Sep 17 00:00:00 2001
From: Zixu Zhang <zixuz@princeton.edu>
Date: Thu, 15 May 2025 15:08:28 -0400
Subject: [PATCH 20/38] fix the wosac eval

---
 examples/eval/run_wosac_eval.py | 64 ++++++++++++++++++++++-----------
 1 file changed, 43 insertions(+), 21 deletions(-)

diff --git a/examples/eval/run_wosac_eval.py b/examples/eval/run_wosac_eval.py
index f18046600..115566e43 100644
--- a/examples/eval/run_wosac_eval.py
+++ b/examples/eval/run_wosac_eval.py
@@ -214,12 +214,13 @@ def rollout(
 
     start_ground_truth_ext = perf_counter()
 
-    scenario_rollouts = []
-    scenario_rollout_masks = []
+    # scenario_rollouts = []
+    joint_scenes = {}
+    # scenario_rollout_masks = []
     for i, scenario_id in enumerate(scenario_ids):
         # control_mask_i = id[i] != 0
         control_mask_i = control_mask[i]
-        scenario_rollout_masks.append(done_stack[i, control_mask_i] == 0)
+        # scenario_rollout_masks.append(done_stack[i, control_mask_i] == 0)
         pos_x_i = pos_x_stack[i, control_mask_i]
         pos_y_i = pos_y_stack[i, control_mask_i]
         pos_z_i = pos_z_stack[i, control_mask_i]
@@ -237,22 +238,28 @@ def rollout(
                     object_id=int(obj_i_a),
                 )
             )
-        joint_scene = sim_agents_submission_pb2.JointScene(
+        # joint_scene = sim_agents_submission_pb2.JointScene(
+        #     simulated_trajectories=simulated_trajectories
+        # )
+
+        joint_scenes[scenario_id] = sim_agents_submission_pb2.JointScene(
             simulated_trajectories=simulated_trajectories
         )
 
-        scenario_rollouts.append(
-            sim_agents_submission_pb2.ScenarioRollouts(
-                joint_scenes=[joint_scene],
-                scenario_id=scenario_id,
-            )
-        )
+        # scenario_rollouts.append(
+        #     sim_agents_submission_pb2.ScenarioRollouts(
+        #         joint_scenes=[joint_scene],
+        #         scenario_id=scenario_id,
+        #     )
+        # )
 
     logging.info(
         f"Ground truth extraction took: {perf_counter() - start_ground_truth_ext:.2f} s ({len(env.data_batch)} scenarios)."
     )
 
-    return scenario_ids, scenario_rollouts, scenario_rollout_masks
+    return joint_scenes
+
+    # return scenario_ids, scenario_rollouts, scenario_rollout_masks
 
 
 def load_config(config_path):
@@ -375,9 +382,9 @@ def load_config(config_path):
     )
 
     for _ in tqdm(range(NUM_DATA_BATCHES)):
+        joint_scene_list = []
         for _ in range(NUM_ROLLOUTS_PER_BATCH):
-
-            scenario_ids, scenario_rollouts, scenario_rollout_masks = rollout(
+            joint_scene = rollout(
                 env=env,
                 sim_agent=agent,
                 init_steps=INIT_STEPS,
@@ -389,16 +396,31 @@ def load_config(config_path):
                 save_videos=RENDER,
                 guidance_mode=GUIDANCE_MODE,
             )
-            tf_record_paths = [
-                os.path.join(DATA_TFRECORD, f"{scenario_id}.tfrecords")
-                for scenario_id in scenario_ids
-            ]
-            wosac_metrics.update(
-                tf_record_paths,
-                scenario_rollouts,
-                # scenario_rollout_masks=scenario_rollout_masks
+            joint_scene_list.append(joint_scene)
+
+        # Construct scenario rollouts
+        scenario_ids = joint_scene_list[0].keys()
+        scenario_rollouts = []
+        for scenario_id in scenario_ids:
+            scenario_rollouts.append(
+                sim_agents_submission_pb2.ScenarioRollouts(
+                    joint_scenes=[
+                        joint_scene[scenario_id] for joint_scene in joint_scene_list
+                    ],
+                    scenario_id=scenario_id,
+                )
             )
 
+        tf_record_paths = [
+            os.path.join(DATA_TFRECORD, f"{scenario_id}.tfrecords")
+            for scenario_id in scenario_ids
+        ]
+        
+        wosac_metrics.update(
+            tf_record_paths,
+            scenario_rollouts,
+        )
+
         # Swap batch of scenarios
         env.swap_data_batch()
 

From ad3ce01cc4e8bce929902073befc3b5297e79ba5 Mon Sep 17 00:00:00 2001
From: Kevin Joseph <kevinwinston184@gmail.com>
Date: Mon, 26 May 2025 23:48:52 -0400
Subject: [PATCH 21/38] fix amortization script (#452)

---
 gpudrive/datatypes/trajectory.py              |   8 +-
 gpudrive/integrations/vbd/data/amortize.py    |  87 +--
 .../vbd/notebooks/04_step_vbd.ipynb           | 562 +++---------------
 gpudrive/integrations/vbd/viz/train.gif       | Bin 65028 -> 0 bytes
 4 files changed, 129 insertions(+), 528 deletions(-)
 delete mode 100644 gpudrive/integrations/vbd/viz/train.gif

diff --git a/gpudrive/datatypes/trajectory.py b/gpudrive/datatypes/trajectory.py
index 7d50b8309..c13a3c220 100644
--- a/gpudrive/datatypes/trajectory.py
+++ b/gpudrive/datatypes/trajectory.py
@@ -206,12 +206,12 @@ def demean_positions(self):
 class VBDTrajectory:
     """A class to represent the VBD predicted trajectories.
     Initialized from `vbd_trajectory_tensor` (src/bindings.cpp).
-    Shape: (num_worlds, max_agents, traj_len, 5)
-            where traj_len is 90 - initialization steps.
+    Shape: (num_worlds, max_agents, traj_len, 6)
+            where traj_len is 91
 
     Attributes:
-        trajectories: Tensor of shape (num_worlds, max_agents, traj_len, 5) containing
-            position (x, y), heading (yaw), and velocity (vx, vy) for each timestep.
+        trajectories: Tensor of shape (num_worlds, max_agents, traj_len, 6) containing
+            position (x, y), heading (yaw), velocity (vx, vy) and valid flag for each timestep.
     """
 
     def __init__(self, vbd_traj_tensor: torch.Tensor):
diff --git a/gpudrive/integrations/vbd/data/amortize.py b/gpudrive/integrations/vbd/data/amortize.py
index b42b879a4..645b1f5ce 100644
--- a/gpudrive/integrations/vbd/data/amortize.py
+++ b/gpudrive/integrations/vbd/data/amortize.py
@@ -49,12 +49,12 @@ def main():
     parser.add_argument(
         "--input_dir",
         help="Directory containing input JSON files",
-        default="data/processed/wosac/validation_json_100",
+        default="data/processed/wosac/validation/json",
     )
     parser.add_argument(
         "--output_dir",
         help="Directory for output JSON files",
-        default="data/processed/wosac/validation_json_100",
+        default="data/processed/validation/json",
     )
     parser.add_argument(
         "--batch_size", type=int, default=10, help="Batch size for processing"
@@ -112,51 +112,52 @@ def main():
         device=device,
     )
 
-    # Final VBD trajectories to write
-    output_trajectories = torch.zeros(
-        gpudrive_env.num_worlds,
-        gpudrive_env.max_agent_count,
-        madrona_gpudrive.kTrajectoryLength,
-        6,
-    )
-
-    # Save init steps from logs
-    log_trajectory = LogTrajectory.from_tensor(
-        gpudrive_env.sim.expert_trajectory_tensor(),
-        num_worlds=gpudrive_env.num_worlds,
-        max_agents=gpudrive_env.max_agent_count,
-        device="cpu",
-    )
 
-    output_trajectories[:, :, : INIT_STEPS + 1, :2] = log_trajectory.pos_xy[
-        :, :, : INIT_STEPS + 1
-    ]
-    output_trajectories[:, :, : INIT_STEPS + 1, 2] = log_trajectory.yaw[
-        :, :, : INIT_STEPS + 1, 0
-    ]
-    output_trajectories[:, :, : INIT_STEPS + 1, 3:5] = log_trajectory.vel_xy[
-        :, :, : INIT_STEPS + 1
-    ]
-    output_trajectories[:, :, : INIT_STEPS + 1, 5] = log_trajectory.valids[
-        :, :, : INIT_STEPS + 1, 0
-    ]
-
-    # Action tensor to step through simulation
-    predicted_actions = torch.zeros(
-        (
+    for _ in range(int(args.num_scenes / args.batch_size)):
+        # Final VBD trajectories to write
+        output_trajectories = torch.zeros(
             gpudrive_env.num_worlds,
             gpudrive_env.max_agent_count,
-            gpudrive_env.episode_len - INIT_STEPS,
-            10,
+            madrona_gpudrive.kTrajectoryLength,
+            6,
         )
-    )
 
-    # World means for VBD outputs
-    world_means = (
-        gpudrive_env.sim.world_means_tensor().to_torch()[:, :2].to("cpu")
-    )
+        # Save init steps from logs
+        log_trajectory = LogTrajectory.from_tensor(
+            gpudrive_env.sim.expert_trajectory_tensor(),
+            num_worlds=gpudrive_env.num_worlds,
+            max_agents=gpudrive_env.max_agent_count,
+            device="cpu",
+        )
+
+        output_trajectories[:, :, : INIT_STEPS + 1, :2] = log_trajectory.pos_xy[
+            :, :, : INIT_STEPS + 1
+        ]
+        output_trajectories[:, :, : INIT_STEPS + 1, 2] = log_trajectory.yaw[
+            :, :, : INIT_STEPS + 1, 0
+        ]
+        output_trajectories[:, :, : INIT_STEPS + 1, 3:5] = log_trajectory.vel_xy[
+            :, :, : INIT_STEPS + 1
+        ]
+        output_trajectories[:, :, : INIT_STEPS + 1, 5] = log_trajectory.valids[
+            :, :, : INIT_STEPS + 1, 0
+        ]
+
+        # Action tensor to step through simulation
+        predicted_actions = torch.zeros(
+            (
+                gpudrive_env.num_worlds,
+                gpudrive_env.max_agent_count,
+                gpudrive_env.episode_len - INIT_STEPS,
+                10,
+            )
+        )
+
+        # World means for VBD outputs
+        world_means = (
+            gpudrive_env.sim.world_means_tensor().to_torch()[:, :2].to("cpu")
+        )
 
-    for _ in range(int(args.num_scenes / args.batch_size)):
         # Generate VBD input
         scene_context = gpudrive_env.construct_context(
             init_steps=gpudrive_env.init_steps
@@ -208,10 +209,10 @@ def main():
             gpudrive_env.sim.absolute_self_observation_tensor(), device="cpu"
         ).id
 
-        filenames = gpudrive_env.get_env_filenames()
+        filenames = gpudrive_env.get_scenario_ids()
 
         for i in range(gpudrive_env.num_worlds):
-            filename = f"{filenames[i]}"
+            filename = f"{filenames[i]}.json"
             with open(f"{args.input_dir}/{filename}", "r") as f:
                 data = json.load(f)
 
diff --git a/gpudrive/integrations/vbd/notebooks/04_step_vbd.ipynb b/gpudrive/integrations/vbd/notebooks/04_step_vbd.ipynb
index 19bf4c99f..8ba504e98 100644
--- a/gpudrive/integrations/vbd/notebooks/04_step_vbd.ipynb
+++ b/gpudrive/integrations/vbd/notebooks/04_step_vbd.ipynb
@@ -52,15 +52,26 @@
     "from gpudrive.env.dataset import SceneDataLoader\n",
     "from gpudrive.env.env_torch import GPUDriveTorchEnv\n",
     "from gpudrive.datatypes.trajectory import VBDTrajectory\n",
-    "from gpudrive.visualize.utils import img_from_fig"
+    "from gpudrive.visualize.utils import img_from_fig\n",
+    "import madrona_gpudrive"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "02325a7d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Smoothing guidance data: 100%|\u001b[34m██████████\u001b[0m| 1/1 [00:00<00:00, 158.12it/s]\n",
+      "Smoothing guidance data: 100%|\u001b[34m██████████\u001b[0m| 1/1 [00:00<00:00, 164.90it/s]\n",
+      "Smoothing guidance data: 100%|\u001b[34m██████████\u001b[0m| 1/1 [00:00<00:00, 61.80it/s]\n"
+     ]
+    }
+   ],
    "source": [
     "#Init GPUDrive env\n",
     "device = \"cpu\"\n",
@@ -68,18 +79,20 @@
     "env_config = EnvConfig(\n",
     "    dynamics_model=\"state\", # Use state-based dynamics model\n",
     "    dist_to_goal_threshold=1e-5, # Trick to make sure the agents don't disappear when they reach the goal\n",
-    "    init_mode = 'all_non_trivial',\n",
+    "    init_mode = 'wosac_eval',\n",
     "    max_controlled_agents=32,\n",
-    "    goal_behavior='ignore'\n",
+    "    goal_behavior='ignore',\n",
+    "    guidance_mode='vbd_amortized'\n",
     ")\n",
     "        \n",
     "# Make env\n",
     "gpudrive_env = GPUDriveTorchEnv(\n",
     "    config=env_config,\n",
     "    data_loader = SceneDataLoader(\n",
-    "        root=\"data/processed/vbd\",\n",
+    "        root=\"data/processed/wosac/validation/json\",\n",
     "        batch_size=1,\n",
-    "        dataset_size=4,\n",
+    "        dataset_size=1,\n",
+    "        file_prefix=\"\"\n",
     "    ),\n",
     "    render_config=RenderConfig(),\n",
     "    max_cont_agents=32, # Maximum number of agents to control per scene\n",
@@ -95,25 +108,25 @@
    "outputs": [],
    "source": [
     "# Action tensor to step through simulation\n",
-    "predicted_actions = torch.zeros((gpudrive_env.num_worlds, gpudrive_env.max_agent_count, gpudrive_env.episode_len, 10))\n",
+    "predicted_actions = torch.zeros((gpudrive_env.num_worlds, gpudrive_env.max_agent_count, madrona_gpudrive.kTrajectoryLength, 10))\n",
     "\n",
     "vbd_trajectories = VBDTrajectory.from_tensor(\n",
     "    gpudrive_env.sim.vbd_trajectory_tensor(),\n",
     "    device=device,\n",
     ")\n",
     "\n",
-    "predicted_actions[:, :, :, 0] = vbd_trajectories.pos_x\n",
-    "predicted_actions[:, :, :, 1] = vbd_trajectories.pos_y\n",
-    "predicted_actions[:, :, :, 3] = vbd_trajectories.yaw\n",
-    "predicted_actions[:, :, :, 4] = vbd_trajectories.vel_x\n",
-    "predicted_actions[:, :, :, 5] = vbd_trajectories.vel_y\n",
+    "predicted_actions[:, :, :, 0] = vbd_trajectories.pos_x.squeeze(-1)\n",
+    "predicted_actions[:, :, :, 1] = vbd_trajectories.pos_y.squeeze(-1)\n",
+    "predicted_actions[:, :, :, 3] = vbd_trajectories.yaw.squeeze(-1)\n",
+    "predicted_actions[:, :, :, 4] = vbd_trajectories.vel_x.squeeze(-1)\n",
+    "predicted_actions[:, :, :, 5] = vbd_trajectories.vel_y.squeeze(-1)\n",
     "\n",
     "sim_state_frames = []\n",
     "fig = gpudrive_env.vis.plot_simulator_state(\n",
     "    time_steps=[0],\n",
     "    env_indices=[0],\n",
-    "    zoom_radius=70,\n",
-    "    plot_vbd_trajectory=True,\n",
+    "    zoom_radius=120,\n",
+    "    plot_guidance_pos_xy=True,\n",
     ")[0]\n",
     "# Save initial state\n",
     "sim_state_frames.append(img_from_fig(fig))\n",
@@ -126,8 +139,8 @@
     "    fig = gpudrive_env.vis.plot_simulator_state(\n",
     "        time_steps=[t],\n",
     "        env_indices=[0],\n",
-    "        zoom_radius=80,\n",
-    "        plot_vbd_trajectory=True,\n",
+    "        zoom_radius=120,\n",
+    "        plot_guidance_pos_xy=True,\n",
     "    )[0]\n",
     "    sim_state_frames.append(img_from_fig(fig))"
    ]
@@ -141,14 +154,11 @@
     {
      "data": {
       "text/plain": [
-       "(tensor([ True,  True,  True,  True, False, False, False, False, False, False,\n",
-       "         False, False,  True, False, False, False, False, False, False, False,\n",
+       "(tensor([ True,  True,  True, False,  True,  True,  True,  True,  True,  True,\n",
+       "          True,  True,  True,  True,  True, False, False, False, False, False,\n",
        "         False, False, False, False, False, False, False, False, False, False,\n",
-       "         False, False, False, False, False, False, False, False, False, False,\n",
-       "         False, False, False, False, False, False, False, False, False, False,\n",
-       "         False, False, False, False, False, False, False, False, False, False,\n",
-       "         False, False, False, False]),\n",
-       " 5)"
+       "         False, False]),\n",
+       " 14)"
       ]
      },
      "execution_count": 4,
@@ -170,465 +180,55 @@
     {
      "data": {
       "text/plain": [
-       "tensor([[[ 3.9034e+01, -1.0506e+01],\n",
-       "         [ 3.8516e+01, -1.0265e+01],\n",
-       "         [ 3.8048e+01, -1.0049e+01],\n",
-       "         [ 3.7587e+01, -9.8340e+00],\n",
-       "         [ 3.7131e+01, -9.6226e+00],\n",
-       "         [ 3.6683e+01, -9.4136e+00],\n",
-       "         [ 3.6242e+01, -9.2080e+00],\n",
-       "         [ 3.5810e+01, -9.0044e+00],\n",
-       "         [ 3.5382e+01, -8.8037e+00],\n",
-       "         [ 3.4963e+01, -8.6055e+00],\n",
-       "         [ 3.4555e+01, -8.4126e+00],\n",
-       "         [ 3.4164e+01, -8.2280e+00],\n",
-       "         [ 3.3766e+01, -8.0400e+00],\n",
-       "         [ 3.3392e+01, -7.8633e+00],\n",
-       "         [ 3.3013e+01, -7.6846e+00],\n",
-       "         [ 3.2659e+01, -7.5171e+00],\n",
-       "         [ 3.2283e+01, -7.3384e+00],\n",
-       "         [ 3.1907e+01, -7.1597e+00],\n",
-       "         [ 3.1518e+01, -6.9731e+00],\n",
-       "         [ 3.1144e+01, -6.7935e+00],\n",
-       "         [ 3.0784e+01, -6.6211e+00],\n",
-       "         [ 3.0405e+01, -6.4380e+00],\n",
-       "         [ 3.0018e+01, -6.2505e+00],\n",
-       "         [ 2.9631e+01, -6.0635e+00],\n",
-       "         [ 2.9224e+01, -5.8657e+00],\n",
-       "         [ 2.8824e+01, -5.6719e+00],\n",
-       "         [ 2.8428e+01, -5.4785e+00],\n",
-       "         [ 2.8018e+01, -5.2788e+00],\n",
-       "         [ 2.7601e+01, -5.0737e+00],\n",
-       "         [ 2.7186e+01, -4.8691e+00],\n",
-       "         [ 2.6741e+01, -4.6494e+00],\n",
-       "         [ 2.6286e+01, -4.4248e+00],\n",
-       "         [ 2.5818e+01, -4.1934e+00],\n",
-       "         [ 2.5336e+01, -3.9531e+00],\n",
-       "         [ 2.4844e+01, -3.7085e+00],\n",
-       "         [ 2.4348e+01, -3.4619e+00],\n",
-       "         [ 2.3834e+01, -3.2061e+00],\n",
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-       "         [ 2.2740e+01, -2.6616e+00],\n",
-       "         [ 2.2169e+01, -2.3765e+00],\n",
-       "         [ 2.1575e+01, -2.0801e+00],\n",
-       "         [ 2.0956e+01, -1.7700e+00],\n",
-       "         [ 2.0326e+01, -1.4536e+00],\n",
-       "         [ 1.9669e+01, -1.1240e+00],\n",
-       "         [ 1.8994e+01, -7.8516e-01],\n",
-       "         [ 1.8307e+01, -4.4092e-01],\n",
-       "         [ 1.7586e+01, -7.7637e-02],\n",
-       "         [ 1.6834e+01,  3.0029e-01],\n",
-       "         [ 1.6077e+01,  6.8213e-01],\n",
-       "         [ 1.5282e+01,  1.0815e+00],\n",
-       "         [ 1.4488e+01,  1.4805e+00],\n",
-       "         [ 1.3668e+01,  1.8921e+00],\n",
-       "         [ 1.2812e+01,  2.3198e+00],\n",
-       "         [ 1.1952e+01,  2.7505e+00],\n",
-       "         [ 1.1073e+01,  3.1875e+00],\n",
-       "         [ 1.0172e+01,  3.6353e+00],\n",
-       "         [ 9.2266e+00,  4.1064e+00],\n",
-       "         [ 8.2725e+00,  4.5791e+00],\n",
-       "         [ 7.3223e+00,  5.0527e+00],\n",
-       "         [ 6.3350e+00,  5.5435e+00],\n",
-       "         [ 5.3271e+00,  6.0439e+00],\n",
-       "         [ 4.3115e+00,  6.5439e+00],\n",
-       "         [ 3.2617e+00,  7.0610e+00],\n",
-       "         [ 2.2090e+00,  7.5781e+00],\n",
-       "         [ 1.1250e+00,  8.1084e+00],\n",
-       "         [ 4.1992e-02,  8.6362e+00],\n",
-       "         [-1.0723e+00,  9.1758e+00],\n",
-       "         [-2.2070e+00,  9.7217e+00],\n",
-       "         [-3.3506e+00,  1.0267e+01],\n",
-       "         [-4.5029e+00,  1.0813e+01],\n",
-       "         [-5.6816e+00,  1.1369e+01],\n",
-       "         [-6.8789e+00,  1.1929e+01],\n",
-       "         [-8.0908e+00,  1.2492e+01],\n",
-       "         [-9.3145e+00,  1.3056e+01],\n",
-       "         [-1.0563e+01,  1.3629e+01],\n",
-       "         [-1.1821e+01,  1.4202e+01],\n",
-       "         [-1.3088e+01,  1.4773e+01],\n",
-       "         [-1.4378e+01,  1.5352e+01],\n",
-       "         [-1.5668e+01,  1.5928e+01],\n",
-       "         [-1.6964e+01,  1.6503e+01],\n",
-       "         [-1.8275e+01,  1.7082e+01],\n",
-       "         [-1.9604e+01,  1.7664e+01],\n",
-       "         [-2.0937e+01,  1.8244e+01],\n",
-       "         [-2.2278e+01,  1.8825e+01],\n",
-       "         [-2.3630e+01,  1.9411e+01],\n",
-       "         [-2.5008e+01,  2.0005e+01],\n",
-       "         [-2.6387e+01,  2.0597e+01],\n",
-       "         [-2.7780e+01,  2.1191e+01],\n",
-       "         [-2.9152e+01,  2.1774e+01],\n",
-       "         [-3.0561e+01,  2.2371e+01],\n",
-       "         [-3.1984e+01,  2.2974e+01]],\n",
+       "tensor([[[ -5.6777,  33.1743],\n",
+       "         [ -6.0010,  33.1763],\n",
+       "         [ -6.3291,  33.1802],\n",
+       "         ...,\n",
+       "         [-24.5342,  33.6284],\n",
+       "         [-24.6396,  33.6313],\n",
+       "         [-24.7119,  33.6333]],\n",
+       "\n",
+       "        [[ -0.9980,  21.9150],\n",
+       "         [ -1.0020,  22.0342],\n",
+       "         [ -0.9746,  22.1816],\n",
+       "         ...,\n",
+       "         [  1.0430,  32.8506],\n",
+       "         [  1.0645,  32.9722],\n",
+       "         [  1.0879,  33.0996]],\n",
+       "\n",
+       "        [[-47.2080,  40.1299],\n",
+       "         [-47.1768,  40.0386],\n",
+       "         [-47.1357,  39.9395],\n",
+       "         ...,\n",
+       "         [-40.8311,  31.8291],\n",
+       "         [-40.7695,  31.7153],\n",
+       "         [-40.7090,  31.6021]],\n",
        "\n",
-       "        [[ 2.2095e+01, -9.8291e+00],\n",
-       "         [ 2.2658e+01, -9.8354e+00],\n",
-       "         [ 2.3247e+01, -9.8486e+00],\n",
-       "         [ 2.3781e+01, -9.9009e+00],\n",
-       "         [ 2.4346e+01, -9.9795e+00],\n",
-       "         [ 2.4896e+01, -1.0084e+01],\n",
-       "         [ 2.5455e+01, -1.0193e+01],\n",
-       "         [ 2.5997e+01, -1.0320e+01],\n",
-       "         [ 2.6554e+01, -1.0462e+01],\n",
-       "         [ 2.7085e+01, -1.0613e+01],\n",
-       "         [ 2.7625e+01, -1.0782e+01],\n",
-       "         [ 2.8198e+01, -1.0951e+01],\n",
-       "         [ 2.8765e+01, -1.1128e+01],\n",
-       "         [ 2.9328e+01, -1.1316e+01],\n",
-       "         [ 2.9914e+01, -1.1522e+01],\n",
-       "         [ 3.0501e+01, -1.1738e+01],\n",
-       "         [ 3.1109e+01, -1.1970e+01],\n",
-       "         [ 3.1724e+01, -1.2211e+01],\n",
-       "         [ 3.2367e+01, -1.2473e+01],\n",
-       "         [ 3.3021e+01, -1.2745e+01],\n",
-       "         [ 3.3693e+01, -1.3032e+01],\n",
-       "         [ 3.4372e+01, -1.3325e+01],\n",
-       "         [ 3.5057e+01, -1.3626e+01],\n",
-       "         [ 3.5753e+01, -1.3936e+01],\n",
-       "         [ 3.6467e+01, -1.4258e+01],\n",
-       "         [ 3.7193e+01, -1.4593e+01],\n",
-       "         [ 3.7931e+01, -1.4937e+01],\n",
-       "         [ 3.8685e+01, -1.5291e+01],\n",
-       "         [ 3.9448e+01, -1.5652e+01],\n",
-       "         [ 4.0217e+01, -1.6017e+01],\n",
-       "         [ 4.1014e+01, -1.6397e+01],\n",
-       "         [ 4.1817e+01, -1.6783e+01],\n",
-       "         [ 4.2635e+01, -1.7177e+01],\n",
-       "         [ 4.3462e+01, -1.7579e+01],\n",
-       "         [ 4.4297e+01, -1.7984e+01],\n",
-       "         [ 4.5142e+01, -1.8394e+01],\n",
-       "         [ 4.6003e+01, -1.8812e+01],\n",
-       "         [ 4.6873e+01, -1.9234e+01],\n",
-       "         [ 4.7744e+01, -1.9656e+01],\n",
-       "         [ 4.8632e+01, -2.0085e+01],\n",
-       "         [ 4.9523e+01, -2.0516e+01],\n",
-       "         [ 5.0414e+01, -2.0944e+01],\n",
-       "         [ 5.1326e+01, -2.1382e+01],\n",
-       "         [ 5.2230e+01, -2.1816e+01],\n",
-       "         [ 5.3135e+01, -2.2249e+01],\n",
-       "         [ 5.4062e+01, -2.2691e+01],\n",
-       "         [ 5.4982e+01, -2.3129e+01],\n",
-       "         [ 5.5902e+01, -2.3567e+01],\n",
-       "         [ 5.6836e+01, -2.4010e+01],\n",
-       "         [ 5.7775e+01, -2.4456e+01],\n",
-       "         [ 5.8713e+01, -2.4901e+01],\n",
-       "         [ 5.9661e+01, -2.5350e+01],\n",
-       "         [ 6.0610e+01, -2.5799e+01],\n",
-       "         [ 6.1544e+01, -2.6238e+01],\n",
-       "         [ 6.2479e+01, -2.6676e+01],\n",
-       "         [ 6.3400e+01, -2.7105e+01],\n",
-       "         [ 6.4317e+01, -2.7531e+01],\n",
-       "         [ 6.5234e+01, -2.7953e+01],\n",
-       "         [ 6.6141e+01, -2.8367e+01],\n",
-       "         [ 6.7046e+01, -2.8778e+01],\n",
-       "         [ 6.7919e+01, -2.9175e+01],\n",
-       "         [ 6.8794e+01, -2.9569e+01],\n",
-       "         [ 6.9645e+01, -2.9952e+01],\n",
-       "         [ 7.0483e+01, -3.0328e+01],\n",
-       "         [ 7.1327e+01, -3.0702e+01],\n",
-       "         [ 7.2135e+01, -3.1055e+01],\n",
-       "         [ 7.2937e+01, -3.1402e+01],\n",
-       "         [ 7.3708e+01, -3.1733e+01],\n",
-       "         [ 7.4448e+01, -3.2046e+01],\n",
-       "         [ 7.5188e+01, -3.2355e+01],\n",
-       "         [ 7.5904e+01, -3.2651e+01],\n",
-       "         [ 7.6585e+01, -3.2930e+01],\n",
-       "         [ 7.7241e+01, -3.3193e+01],\n",
-       "         [ 7.7884e+01, -3.3450e+01],\n",
-       "         [ 7.8489e+01, -3.3688e+01],\n",
-       "         [ 7.9070e+01, -3.3915e+01],\n",
-       "         [ 7.9623e+01, -3.4128e+01],\n",
-       "         [ 8.0130e+01, -3.4320e+01],\n",
-       "         [ 8.0604e+01, -3.4498e+01],\n",
-       "         [ 8.1030e+01, -3.4652e+01],\n",
-       "         [ 8.1439e+01, -3.4797e+01],\n",
-       "         [ 8.1807e+01, -3.4925e+01],\n",
-       "         [ 8.2109e+01, -3.5027e+01],\n",
-       "         [ 8.2387e+01, -3.5121e+01],\n",
-       "         [ 8.2588e+01, -3.5189e+01],\n",
-       "         [ 8.2708e+01, -3.5229e+01],\n",
-       "         [ 8.2764e+01, -3.5249e+01],\n",
-       "         [ 8.2764e+01, -3.5249e+01],\n",
-       "         [ 8.2764e+01, -3.5249e+01],\n",
-       "         [ 8.2764e+01, -3.5249e+01],\n",
-       "         [ 8.2764e+01, -3.5249e+01]],\n",
+       "        ...,\n",
        "\n",
-       "        [[ 2.9411e+01, -2.5859e+00],\n",
-       "         [ 2.9122e+01, -2.4419e+00],\n",
-       "         [ 2.8792e+01, -2.3032e+00],\n",
-       "         [ 2.8470e+01, -2.1450e+00],\n",
-       "         [ 2.8105e+01, -1.9780e+00],\n",
-       "         [ 2.7748e+01, -1.8140e+00],\n",
-       "         [ 2.7390e+01, -1.6353e+00],\n",
-       "         [ 2.6999e+01, -1.4526e+00],\n",
-       "         [ 2.6599e+01, -1.2539e+00],\n",
-       "         [ 2.6185e+01, -1.0479e+00],\n",
-       "         [ 2.5748e+01, -8.3154e-01],\n",
-       "         [ 2.5294e+01, -6.0840e-01],\n",
-       "         [ 2.4824e+01, -3.7695e-01],\n",
-       "         [ 2.4339e+01, -1.3525e-01],\n",
-       "         [ 2.3841e+01,  1.1279e-01],\n",
-       "         [ 2.3313e+01,  3.7598e-01],\n",
-       "         [ 2.2790e+01,  6.3721e-01],\n",
-       "         [ 2.2256e+01,  9.0479e-01],\n",
-       "         [ 2.1700e+01,  1.1836e+00],\n",
-       "         [ 2.1130e+01,  1.4712e+00],\n",
-       "         [ 2.0547e+01,  1.7656e+00],\n",
-       "         [ 1.9934e+01,  2.0776e+00],\n",
-       "         [ 1.9317e+01,  2.3921e+00],\n",
-       "         [ 1.8679e+01,  2.7183e+00],\n",
-       "         [ 1.8043e+01,  3.0444e+00],\n",
-       "         [ 1.7397e+01,  3.3755e+00],\n",
-       "         [ 1.6722e+01,  3.7227e+00],\n",
-       "         [ 1.6045e+01,  4.0713e+00],\n",
-       "         [ 1.5345e+01,  4.4321e+00],\n",
-       "         [ 1.4638e+01,  4.7964e+00],\n",
-       "         [ 1.3927e+01,  5.1626e+00],\n",
-       "         [ 1.3190e+01,  5.5420e+00],\n",
-       "         [ 1.2453e+01,  5.9224e+00],\n",
-       "         [ 1.1694e+01,  6.3130e+00],\n",
-       "         [ 1.0953e+01,  6.6943e+00],\n",
-       "         [ 1.0185e+01,  7.0879e+00],\n",
-       "         [ 9.4092e+00,  7.4863e+00],\n",
-       "         [ 8.6182e+00,  7.8921e+00],\n",
-       "         [ 7.8135e+00,  8.3042e+00],\n",
-       "         [ 6.9971e+00,  8.7217e+00],\n",
-       "         [ 6.1787e+00,  9.1406e+00],\n",
-       "         [ 5.3408e+00,  9.5664e+00],\n",
-       "         [ 4.4834e+00,  1.0002e+01],\n",
-       "         [ 3.6318e+00,  1.0431e+01],\n",
-       "         [ 2.7725e+00,  1.0863e+01],\n",
-       "         [ 1.8857e+00,  1.1307e+01],\n",
-       "         [ 9.7852e-01,  1.1757e+01],\n",
-       "         [ 6.4453e-02,  1.2210e+01],\n",
-       "         [-8.5254e-01,  1.2663e+01],\n",
-       "         [-1.7686e+00,  1.3111e+01],\n",
-       "         [-2.6973e+00,  1.3562e+01],\n",
-       "         [-3.6406e+00,  1.4017e+01],\n",
-       "         [-4.5957e+00,  1.4475e+01],\n",
-       "         [-5.5537e+00,  1.4930e+01],\n",
-       "         [-6.5244e+00,  1.5388e+01],\n",
-       "         [-7.5117e+00,  1.5848e+01],\n",
-       "         [-8.4961e+00,  1.6303e+01],\n",
-       "         [-9.4893e+00,  1.6761e+01],\n",
-       "         [-1.0494e+01,  1.7220e+01],\n",
-       "         [-1.1495e+01,  1.7673e+01],\n",
-       "         [-1.2526e+01,  1.8137e+01],\n",
-       "         [-1.3569e+01,  1.8604e+01],\n",
-       "         [-1.4630e+01,  1.9075e+01],\n",
-       "         [-1.5688e+01,  1.9542e+01],\n",
-       "         [-1.6763e+01,  2.0010e+01],\n",
-       "         [-1.7834e+01,  2.0473e+01],\n",
-       "         [-1.8931e+01,  2.0944e+01],\n",
-       "         [-2.0040e+01,  2.1414e+01],\n",
-       "         [-2.1160e+01,  2.1882e+01],\n",
-       "         [-2.2275e+01,  2.2347e+01],\n",
-       "         [-2.3398e+01,  2.2810e+01],\n",
-       "         [-2.4543e+01,  2.3277e+01],\n",
-       "         [-2.5725e+01,  2.3756e+01],\n",
-       "         [-2.6895e+01,  2.4227e+01],\n",
-       "         [-2.8081e+01,  2.4700e+01],\n",
-       "         [-2.9251e+01,  2.5163e+01],\n",
-       "         [-3.0437e+01,  2.5628e+01],\n",
-       "         [-3.1641e+01,  2.6098e+01],\n",
-       "         [-3.2861e+01,  2.6569e+01],\n",
-       "         [-3.4087e+01,  2.7036e+01],\n",
-       "         [-3.5321e+01,  2.7507e+01],\n",
-       "         [-3.6555e+01,  2.7975e+01],\n",
-       "         [-3.7812e+01,  2.8447e+01],\n",
-       "         [-3.9090e+01,  2.8927e+01],\n",
-       "         [-4.0373e+01,  2.9409e+01],\n",
-       "         [-4.1665e+01,  2.9892e+01],\n",
-       "         [-4.2946e+01,  3.0368e+01],\n",
-       "         [-4.4248e+01,  3.0851e+01],\n",
-       "         [-4.5574e+01,  3.1340e+01],\n",
-       "         [-4.6872e+01,  3.1814e+01],\n",
-       "         [-4.8202e+01,  3.2301e+01]],\n",
+       "        [[  0.0000,   0.0000],\n",
+       "         [  0.0000,   0.0000],\n",
+       "         [  0.0000,   0.0000],\n",
+       "         ...,\n",
+       "         [  0.0000,   0.0000],\n",
+       "         [  0.0000,   0.0000],\n",
+       "         [  0.0000,   0.0000]],\n",
        "\n",
-       "        [[ 4.0624e+01, -1.6779e+01],\n",
-       "         [ 4.1490e+01, -1.7136e+01],\n",
-       "         [ 4.2452e+01, -1.7558e+01],\n",
-       "         [ 4.3405e+01, -1.7994e+01],\n",
-       "         [ 4.4389e+01, -1.8441e+01],\n",
-       "         [ 4.5390e+01, -1.8905e+01],\n",
-       "         [ 4.6364e+01, -1.9345e+01],\n",
-       "         [ 4.7404e+01, -1.9812e+01],\n",
-       "         [ 4.8429e+01, -2.0269e+01],\n",
-       "         [ 4.9470e+01, -2.0736e+01],\n",
-       "         [ 5.0501e+01, -2.1190e+01],\n",
-       "         [ 5.1555e+01, -2.1662e+01],\n",
-       "         [ 5.2633e+01, -2.2144e+01],\n",
-       "         [ 5.3697e+01, -2.2618e+01],\n",
-       "         [ 5.4791e+01, -2.3106e+01],\n",
-       "         [ 5.5912e+01, -2.3606e+01],\n",
-       "         [ 5.7061e+01, -2.4120e+01],\n",
-       "         [ 5.8196e+01, -2.4631e+01],\n",
-       "         [ 5.9352e+01, -2.5151e+01],\n",
-       "         [ 6.0508e+01, -2.5673e+01],\n",
-       "         [ 6.1690e+01, -2.6208e+01],\n",
-       "         [ 6.2876e+01, -2.6745e+01],\n",
-       "         [ 6.4058e+01, -2.7282e+01],\n",
-       "         [ 6.5268e+01, -2.7833e+01],\n",
-       "         [ 6.6478e+01, -2.8386e+01],\n",
-       "         [ 6.7688e+01, -2.8940e+01],\n",
-       "         [ 6.8909e+01, -2.9500e+01],\n",
-       "         [ 7.0140e+01, -3.0061e+01],\n",
-       "         [ 7.1382e+01, -3.0628e+01],\n",
-       "         [ 7.2608e+01, -3.1185e+01],\n",
-       "         [ 7.3817e+01, -3.1731e+01],\n",
-       "         [ 7.5029e+01, -3.2276e+01],\n",
-       "         [ 7.6245e+01, -3.2826e+01],\n",
-       "         [ 7.7442e+01, -3.3365e+01],\n",
-       "         [ 7.8629e+01, -3.3898e+01],\n",
-       "         [ 7.9819e+01, -3.4433e+01],\n",
-       "         [ 8.1011e+01, -3.4972e+01],\n",
-       "         [ 8.2187e+01, -3.5503e+01],\n",
-       "         [ 8.3336e+01, -3.6022e+01],\n",
-       "         [ 8.4464e+01, -3.6533e+01],\n",
-       "         [ 8.5568e+01, -3.7036e+01],\n",
-       "         [ 8.6664e+01, -3.7530e+01],\n",
-       "         [ 8.7739e+01, -3.8013e+01],\n",
-       "         [ 8.8778e+01, -3.8478e+01],\n",
-       "         [ 8.9790e+01, -3.8927e+01],\n",
-       "         [ 9.0780e+01, -3.9365e+01],\n",
-       "         [ 9.1742e+01, -3.9791e+01],\n",
-       "         [ 9.2688e+01, -4.0209e+01],\n",
-       "         [ 9.3594e+01, -4.0607e+01],\n",
-       "         [ 9.4469e+01, -4.0991e+01],\n",
-       "         [ 9.5320e+01, -4.1363e+01],\n",
-       "         [ 9.6114e+01, -4.1710e+01],\n",
-       "         [ 9.6881e+01, -4.2047e+01],\n",
-       "         [ 9.7606e+01, -4.2366e+01],\n",
-       "         [ 9.8307e+01, -4.2675e+01],\n",
-       "         [ 9.8972e+01, -4.2969e+01],\n",
-       "         [ 9.9583e+01, -4.3242e+01],\n",
-       "         [ 1.0015e+02, -4.3494e+01],\n",
-       "         [ 1.0065e+02, -4.3716e+01],\n",
-       "         [ 1.0109e+02, -4.3917e+01],\n",
-       "         [ 1.0150e+02, -4.4100e+01],\n",
-       "         [ 1.0185e+02, -4.4256e+01],\n",
-       "         [ 1.0216e+02, -4.4393e+01],\n",
-       "         [ 1.0242e+02, -4.4506e+01],\n",
-       "         [ 1.0262e+02, -4.4592e+01],\n",
-       "         [ 1.0273e+02, -4.4643e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01],\n",
-       "         [ 1.0274e+02, -4.4646e+01]],\n",
+       "        [[-78.5566,  33.8350],\n",
+       "         [-78.4814,  33.9312],\n",
+       "         [-78.4121,  34.0332],\n",
+       "         ...,\n",
+       "         [-76.4219,  41.7549],\n",
+       "         [-76.3252,  41.8037],\n",
+       "         [-76.2188,  41.8569]],\n",
        "\n",
-       "        [[ 8.3834e+01, -3.5559e+01],\n",
-       "         [ 8.4757e+01, -3.5976e+01],\n",
-       "         [ 8.5865e+01, -3.6500e+01],\n",
-       "         [ 8.6922e+01, -3.7000e+01],\n",
-       "         [ 8.8012e+01, -3.7529e+01],\n",
-       "         [ 8.9077e+01, -3.8047e+01],\n",
-       "         [ 9.0172e+01, -3.8581e+01],\n",
-       "         [ 9.1202e+01, -3.9049e+01],\n",
-       "         [ 9.2308e+01, -3.9602e+01],\n",
-       "         [ 9.3380e+01, -4.0129e+01],\n",
-       "         [ 9.4440e+01, -4.0639e+01],\n",
-       "         [ 8.3713e+03, -8.1131e+03],\n",
-       "         [ 8.3713e+03, -8.1131e+03],\n",
-       "         [ 8.3713e+03, -8.1131e+03],\n",
-       "         [ 8.3713e+03, -8.1131e+03],\n",
-       "         [ 8.3713e+03, -8.1131e+03],\n",
-       "         [ 8.3713e+03, -8.1131e+03],\n",
-       "         [ 8.3713e+03, -8.1131e+03],\n",
-       "         [ 8.3713e+03, -8.1131e+03],\n",
-       "         [ 8.3714e+03, -8.1131e+03],\n",
-       "         [ 8.3714e+03, -8.1131e+03],\n",
-       "         [ 8.3714e+03, -8.1131e+03],\n",
-       "         [ 8.3714e+03, -8.1131e+03],\n",
-       "         [ 8.3715e+03, -8.1131e+03],\n",
-       "         [ 8.3715e+03, -8.1131e+03],\n",
-       "         [ 8.3715e+03, -8.1131e+03],\n",
-       "         [ 8.3716e+03, -8.1131e+03],\n",
-       "         [ 8.3716e+03, -8.1131e+03],\n",
-       "         [ 8.3717e+03, -8.1131e+03],\n",
-       "         [ 8.3718e+03, -8.1131e+03],\n",
-       "         [ 8.3719e+03, -8.1131e+03],\n",
-       "         [ 8.3719e+03, -8.1131e+03],\n",
-       "         [ 8.3720e+03, -8.1131e+03],\n",
-       "         [ 8.3721e+03, -8.1131e+03],\n",
-       "         [ 8.3722e+03, -8.1131e+03],\n",
-       "         [ 8.3722e+03, -8.1131e+03],\n",
-       "         [ 8.3723e+03, -8.1131e+03],\n",
-       "         [ 8.3724e+03, -8.1131e+03],\n",
-       "         [ 8.3725e+03, -8.1131e+03],\n",
-       "         [ 8.3726e+03, -8.1131e+03],\n",
-       "         [ 8.3728e+03, -8.1131e+03],\n",
-       "         [ 8.3729e+03, -8.1131e+03],\n",
-       "         [ 8.3730e+03, -8.1131e+03],\n",
-       "         [ 8.3731e+03, -8.1131e+03],\n",
-       "         [ 8.3733e+03, -8.1131e+03],\n",
-       "         [ 8.3734e+03, -8.1131e+03],\n",
-       "         [ 8.3735e+03, -8.1131e+03],\n",
-       "         [ 8.3736e+03, -8.1131e+03],\n",
-       "         [ 8.3738e+03, -8.1131e+03],\n",
-       "         [ 8.3739e+03, -8.1131e+03],\n",
-       "         [ 8.3741e+03, -8.1131e+03],\n",
-       "         [ 8.3742e+03, -8.1131e+03],\n",
-       "         [ 8.3743e+03, -8.1131e+03],\n",
-       "         [ 8.3744e+03, -8.1131e+03],\n",
-       "         [ 8.3746e+03, -8.1131e+03],\n",
-       "         [ 8.3747e+03, -8.1131e+03],\n",
-       "         [ 8.3749e+03, -8.1131e+03],\n",
-       "         [ 8.3750e+03, -8.1131e+03],\n",
-       "         [ 8.3751e+03, -8.1131e+03],\n",
-       "         [ 8.3752e+03, -8.1131e+03],\n",
-       "         [ 8.3753e+03, -8.1131e+03],\n",
-       "         [ 8.3754e+03, -8.1131e+03],\n",
-       "         [ 8.3755e+03, -8.1131e+03],\n",
-       "         [ 8.3756e+03, -8.1131e+03],\n",
-       "         [ 8.3757e+03, -8.1131e+03],\n",
-       "         [ 8.3758e+03, -8.1131e+03],\n",
-       "         [ 8.3758e+03, -8.1131e+03],\n",
-       "         [ 8.3759e+03, -8.1131e+03],\n",
-       "         [ 8.3759e+03, -8.1131e+03],\n",
-       "         [ 8.3760e+03, -8.1131e+03],\n",
-       "         [ 8.3760e+03, -8.1131e+03],\n",
-       "         [ 8.3761e+03, -8.1131e+03],\n",
-       "         [ 8.3761e+03, -8.1131e+03],\n",
-       "         [ 8.3761e+03, -8.1131e+03],\n",
-       "         [ 8.3761e+03, -8.1131e+03],\n",
-       "         [ 8.3761e+03, -8.1131e+03],\n",
-       "         [ 8.3761e+03, -8.1131e+03],\n",
-       "         [ 8.3761e+03, -8.1131e+03],\n",
-       "         [ 8.3761e+03, -8.1131e+03],\n",
-       "         [ 8.3761e+03, -8.1131e+03],\n",
-       "         [ 8.3761e+03, -8.1131e+03],\n",
-       "         [ 8.3761e+03, -8.1131e+03],\n",
-       "         [ 8.3762e+03, -8.1131e+03],\n",
-       "         [ 8.3762e+03, -8.1131e+03],\n",
-       "         [ 8.3762e+03, -8.1131e+03],\n",
-       "         [ 8.3762e+03, -8.1131e+03],\n",
-       "         [ 8.3763e+03, -8.1131e+03],\n",
-       "         [ 8.3763e+03, -8.1131e+03],\n",
-       "         [ 8.3763e+03, -8.1131e+03],\n",
-       "         [ 8.3763e+03, -8.1131e+03],\n",
-       "         [ 8.3763e+03, -8.1131e+03]]])"
+       "        [[  4.9863,  42.4492],\n",
+       "         [  5.0293,  42.4150],\n",
+       "         [  4.9941,  42.2925],\n",
+       "         ...,\n",
+       "         [  5.0830,  41.1494],\n",
+       "         [  5.0830,  41.1494],\n",
+       "         [  5.0830,  41.1494]]])"
       ]
      },
      "execution_count": 5,
@@ -643,12 +243,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "19a5bca6",
    "metadata": {},
    "outputs": [],
    "source": [
-    "mediapy.write_video('gpudrive/integrations/vbd/viz/train.gif', sim_state_frames, fps=20, codec=\"gif\")"
+    "mediapy.write_video('gpudrive/integrations/vbd/viz/eval.gif', sim_state_frames, fps=20, codec=\"gif\")"
    ]
   }
  ],
diff --git a/gpudrive/integrations/vbd/viz/train.gif b/gpudrive/integrations/vbd/viz/train.gif
deleted file mode 100644
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Ke1_2={QnEi!4v-g


From 56529c2ed85cf3949399035550f896f46fc86824 Mon Sep 17 00:00:00 2001
From: Pragnay Mandavilli <108453901+mpragnay@users.noreply.github.com>
Date: Fri, 30 May 2025 09:49:42 -0400
Subject: [PATCH 22/38] Added configs, params and code for the view cone
 setting (#454)

Co-authored-by: Pragnay Mandavilli <pm3881@ga014.hpc.nyu.edu>
---
 gpudrive/env/base_env.py   |  1 +
 gpudrive/env/config.py     |  1 +
 gpudrive/env/env_torch.py  | 28 +---------------------------
 gpudrive/visualize/core.py | 12 ++++++++++++
 src/bindings.cpp           |  1 +
 src/init.hpp               |  1 +
 src/sim.cpp                | 10 ++++++++++
 7 files changed, 27 insertions(+), 27 deletions(-)

diff --git a/gpudrive/env/base_env.py b/gpudrive/env/base_env.py
index cb99f21ff..1676fa4e7 100755
--- a/gpudrive/env/base_env.py
+++ b/gpudrive/env/base_env.py
@@ -86,6 +86,7 @@ def _set_road_reduction_params(self, params):
             object: Updated parameters object with road reduction settings.
         """
         params.observationRadius = self.config.obs_radius
+        params.viewConeHalfAngle = self.config.view_cone_half_angle
         if self.config.road_obs_algorithm == "k_nearest_roadpoints":
             params.roadObservationAlgorithm = (
                 madrona_gpudrive.FindRoadObservationsWith.KNearestEntitiesWithRadiusFiltering
diff --git a/gpudrive/env/config.py b/gpudrive/env/config.py
index 2c4f9733d..2a83d6ac9 100755
--- a/gpudrive/env/config.py
+++ b/gpudrive/env/config.py
@@ -57,6 +57,7 @@ class EnvConfig:
     # Road observation algorithm settings
     road_obs_algorithm: str = "linear"  # Algorithm for road observations
     obs_radius: float = 50.0  # Radius for road observations
+    view_cone_half_angle: float = torch.pi  # Half-angle for view cone setting
     polyline_reduction_threshold: float = (
         0.1  # Threshold for polyline reduction
     )
diff --git a/gpudrive/env/env_torch.py b/gpudrive/env/env_torch.py
index 6d7decab2..22f2f8b98 100755
--- a/gpudrive/env/env_torch.py
+++ b/gpudrive/env/env_torch.py
@@ -1876,32 +1876,6 @@ def render(self, focus_env_idx=0, focus_agent_idx=[0, 1]):
 
         return sim_states, agent_views
 
-    def render(self, focus_env_idx=0, focus_agent_idx=[0, 1]):
-        """Quick rendering function for debugging."""
-
-        sim_states = self.vis.plot_simulator_state(
-            env_indices=[focus_env_idx],
-            zoom_radius=70,
-            time_steps=[self.step_in_world[0, 0, 0].item()],
-            plot_guidance_pos_xy=True,
-        )
-
-        agent_views = []
-        for agent_idx in focus_agent_idx:
-            agent_obs = self.vis.plot_agent_observation(
-                env_idx=focus_env_idx,
-                agent_idx=agent_idx,
-                figsize=(10, 10),
-                trajectory=self.reference_path[agent_idx, :, :],
-                step_reward=self.guidance_reward[
-                    focus_env_idx, agent_idx
-                ].item(),
-                route_progress=self.route_progress[agent_idx],
-            )
-            agent_views.append(agent_obs)
-
-        return sim_states, agent_views
-
 
 if __name__ == "__main__":
 
@@ -1924,7 +1898,7 @@ def render(self, focus_env_idx=0, focus_agent_idx=[0, 1]):
 
     # Create data loader
     train_loader = SceneDataLoader(
-        root="data/processed/wosac/validation_json_100",
+        root="data/processed/validation",
         batch_size=1,
         dataset_size=100,
         sample_with_replacement=False,
diff --git a/gpudrive/visualize/core.py b/gpudrive/visualize/core.py
index b52dc8914..2cebb1312 100644
--- a/gpudrive/visualize/core.py
+++ b/gpudrive/visualize/core.py
@@ -6,6 +6,7 @@
 import seaborn as sns
 import matplotlib.pyplot as plt
 from matplotlib.patches import Circle
+from matplotlib.lines import Line2D
 from matplotlib.collections import LineCollection
 from mpl_toolkits.mplot3d import Axes3D
 from mpl_toolkits.mplot3d.art3d import Poly3DCollection, Line3DCollection
@@ -2024,6 +2025,17 @@ def plot_agent_observation(
         )
 
         ax.add_patch(observation_circle)
+
+        # Add V lines for view cone boundaries
+        x1 = self.env_config.obs_radius * np.cos(self.env_config.view_cone_half_angle)
+        y1 = self.env_config.obs_radius * np.sin(self.env_config.view_cone_half_angle)
+
+        x2 = self.env_config.obs_radius * np.cos(-self.env_config.view_cone_half_angle)
+        y2 = self.env_config.obs_radius * np.sin(-self.env_config.view_cone_half_angle)
+
+        ax.add_line(Line2D([0, x1], [0, y1], color="#FF9900", linewidth=1.5, linestyle="--"))
+        ax.add_line(Line2D([0, x2], [0, y2], color="#FF9900", linewidth=1.5, linestyle="--"))
+
         ax.set_xticks([])
         ax.set_yticks([])
         plt.axis("off")
diff --git a/src/bindings.cpp b/src/bindings.cpp
index d725606b3..e5b5a1af0 100755
--- a/src/bindings.cpp
+++ b/src/bindings.cpp
@@ -50,6 +50,7 @@ namespace madrona_gpudrive
             .def(nb::init<>()) // Default constructor
             .def_rw("polylineReductionThreshold", &Parameters::polylineReductionThreshold)
             .def_rw("observationRadius", &Parameters::observationRadius)
+            .def_rw("viewConeHalfAngle", &Parameters::viewConeHalfAngle)
             .def_rw("rewardParams", &Parameters::rewardParams)
             .def_rw("collisionBehaviour", &Parameters::collisionBehaviour)
             .def_rw("goalBehaviour", &Parameters::goalBehaviour)
diff --git a/src/init.hpp b/src/init.hpp
index 15b19dc11..5eb6a8ece 100755
--- a/src/init.hpp
+++ b/src/init.hpp
@@ -119,6 +119,7 @@ namespace madrona_gpudrive
     {
         float polylineReductionThreshold;
         float observationRadius;
+        float viewConeHalfAngle;
         RewardParams rewardParams;
         CollisionBehaviour collisionBehaviour = CollisionBehaviour::AgentStop; // Default: AgentStop
         GoalBehaviour goalBehaviour = GoalBehaviour::Remove;  // Default to current behavior
diff --git a/src/sim.cpp b/src/sim.cpp
index fecef682e..7a1a6a6e2 100755
--- a/src/sim.cpp
+++ b/src/sim.cpp
@@ -229,6 +229,16 @@ inline void collectPartnerObsSystem(Engine &ctx,
         {
             continue;
         }
+
+        float cos_angle = relative_pos.x/relative_pos.length();
+        float angle = std::acos(cos_angle);
+
+        // Only include agents that are within the view cone
+        if(angle > ctx.data().params.viewConeHalfAngle)
+        {
+            continue;
+        }
+
         partner_obs.obs[arrIndex++] = {
             .speed = relative_speed,
             .position = relative_pos,

From 3db4debde18f165a5d36280899d495bfe5507e6b Mon Sep 17 00:00:00 2001
From: Daphne Cornelisse <33460159+daphne-cornelisse@users.noreply.github.com>
Date: Sun, 1 Jun 2025 16:45:16 -0700
Subject: [PATCH 23/38] Type-aware action space (#455)

* Testing agent type in obs

* Implement type-aware action space

* Remove padding agent

* Cleanup

* Comment out cone view line for now

* Update angle ranges
---
 baselines/ppo/config/ppo_guided_autonomy.yaml |   6 +-
 .../eval/notebooks/obtain_guidance_data.py    |  11 +-
 examples/eval/run_wosac_eval.py               |  24 +-
 gpudrive/datatypes/observation.py             |   2 +-
 gpudrive/env/config.py                        |  32 +-
 gpudrive/env/env_puffer.py                    |  16 -
 gpudrive/env/env_torch.py                     | 274 ++++++++++++++----
 gpudrive/visualize/core.py                    |  12 +-
 8 files changed, 259 insertions(+), 118 deletions(-)

diff --git a/baselines/ppo/config/ppo_guided_autonomy.yaml b/baselines/ppo/config/ppo_guided_autonomy.yaml
index f7d134cb7..c87ea916c 100644
--- a/baselines/ppo/config/ppo_guided_autonomy.yaml
+++ b/baselines/ppo/config/ppo_guided_autonomy.yaml
@@ -2,7 +2,7 @@ mode: "train"
 use_rnn: false
 eval_model_path: null
 baseline: false
-data_dir: data/processed/wosac/validation_json_1
+data_dir: data/processed/wosac/validation_interactive/json
 continue_training: false
 model_cpt: null
 
@@ -44,8 +44,6 @@ environment: # Overrides default environment configs (see pygpudrive/env/config.
   obs_radius: 50.0 # Visibility radius of the agents
   action_space_steer_disc: 13
   action_space_accel_disc: 7
-  max_steer_angle: 1.57  # pi/2 = 1.57, pi/3 = 1.05
-  max_accel_value: 4.0
   init_steps: 0 # Warmup steps
   goal_achieved_weight: 0.0
 
@@ -111,7 +109,7 @@ train:
   # # # Rendering # # #
   render: true # Determines whether to render the environment (note: will slow down training)
   render_3d: false # Render simulator state in 3d or 2d
-  render_interval: 200 # Render every k iterations
+  render_interval: 10 # Render every k iterations
   render_every_t: 5 # Render every k timesteps
   render_k_scenarios: 1 # Number of scenarios to render
   render_agent_idx: [0] # Agent observations to render
diff --git a/examples/eval/notebooks/obtain_guidance_data.py b/examples/eval/notebooks/obtain_guidance_data.py
index d70863863..dcbd405e7 100644
--- a/examples/eval/notebooks/obtain_guidance_data.py
+++ b/examples/eval/notebooks/obtain_guidance_data.py
@@ -7,12 +7,12 @@
 from gpudrive.env.dataset import SceneDataLoader
 import torch
 import numpy as np
-
+from gpudrive.datatypes.observation import LocalEgoState
 
 if __name__ == "__main__":
 
-    GUIDANCE_MODE = "vbd_online"
-    DATASET = "data/processed/wosac/validation_json_100"  # Ensure VBD trajectory structures are in here
+    GUIDANCE_MODE = "log_replay"
+    DATASET = "data/processed/wosac/validation_interactive/json"  # Ensure VBD trajectory structures are in here
     SAVE_PATH = "examples/eval/figures_data/guidance/"
 
     env_config = EnvConfig(
@@ -30,7 +30,7 @@
 
     train_loader = SceneDataLoader(
         root=DATASET,
-        batch_size=10,
+        batch_size=1,
         dataset_size=100,
         sample_with_replacement=False,
         shuffle=False,
@@ -41,7 +41,7 @@
         config=env_config,
         data_loader=train_loader,
         max_cont_agents=32,
-        device="cuda",
+        device="cpu",
     )
 
     obs = env.reset(env.cont_agent_mask)
@@ -53,6 +53,7 @@
             env.reference_trajectory.vel_xy,
             env.reference_trajectory.yaw,
             env.reference_trajectory.valids,
+            env.agent_types,
         ],
         dim=-1,
     )
diff --git a/examples/eval/run_wosac_eval.py b/examples/eval/run_wosac_eval.py
index 1d238d7f9..3a94e8cfa 100644
--- a/examples/eval/run_wosac_eval.py
+++ b/examples/eval/run_wosac_eval.py
@@ -336,27 +336,8 @@ def load_config(config_path):
         remove_non_vehicles=config.remove_non_vehicles,
         lidar_obs=False,
         obs_radius=config.obs_radius,
-        max_steer_angle=config.max_steer_angle,
-        max_accel_value=config.max_accel_value,
         action_space_steer_disc=config.action_space_steer_disc,
         action_space_accel_disc=config.action_space_accel_disc,
-        # Override action space
-        steer_actions=torch.round(
-            torch.linspace(
-                -config.max_steer_angle,
-                config.max_steer_angle,
-                config.action_space_steer_disc,
-            ),
-            decimals=3,
-        ),
-        accel_actions=torch.round(
-            torch.linspace(
-                -config.max_accel_value,
-                config.max_accel_value,
-                config.action_space_accel_disc,
-            ),
-            decimals=3,
-        ),
         init_mode="wosac_eval",
         init_steps=INIT_STEPS,
         guidance_mode=GUIDANCE_MODE,
@@ -404,7 +385,8 @@ def load_config(config_path):
             scenario_rollouts.append(
                 sim_agents_submission_pb2.ScenarioRollouts(
                     joint_scenes=[
-                        joint_scene[scenario_id] for joint_scene in joint_scene_list
+                        joint_scene[scenario_id]
+                        for joint_scene in joint_scene_list
                     ],
                     scenario_id=scenario_id,
                 )
@@ -414,7 +396,7 @@ def load_config(config_path):
             os.path.join(DATA_TFRECORD, f"{scenario_id}.tfrecords")
             for scenario_id in scenario_ids
         ]
-        
+
         wosac_metrics.update(
             tf_record_paths,
             scenario_rollouts,
diff --git a/gpudrive/datatypes/observation.py b/gpudrive/datatypes/observation.py
index 36a5bf61b..0573cc25c 100644
--- a/gpudrive/datatypes/observation.py
+++ b/gpudrive/datatypes/observation.py
@@ -89,7 +89,7 @@ def normalize(self):
             min_val=constants.MIN_REL_GOAL_COORD,
             max_val=constants.MAX_REL_GOAL_COORD,
         )
-        self.steer_angle /= (torch.pi / 3)
+        self.steer_angle /= torch.pi / 3
 
     @property
     def shape(self) -> tuple[int, ...]:
diff --git a/gpudrive/env/config.py b/gpudrive/env/config.py
index 2a83d6ac9..9300c21d6 100755
--- a/gpudrive/env/config.py
+++ b/gpudrive/env/config.py
@@ -68,24 +68,26 @@ class EnvConfig:
     )
 
     # Action space settings (if discretized)
+    # Type-aware action space settings
+    use_type_aware_actions: bool = True  # Toggles type-aware action mapping: if False, use vehicle ranges for all agents
+
+    # Vehicle action ranges
+    vehicle_accel_range: Tuple[float, float] = (-4.0, 4.0)  # m/s²
+    vehicle_steer_range: Tuple[float, float] = (-1.57, 1.57)  # radians
+
+    # Cyclist action ranges
+    cyclist_accel_range: Tuple[float, float] = (-2.5, 2.5)    # m/s²
+    cyclist_steer_range: Tuple[float, float] = (-2.09, 2.09)  # radians (±120°)
+
+    # Pedestrian action ranges
+    pedestrian_accel_range: Tuple[float, float] = (-1.5, 1.5)  # m/s²
+    pedestrian_steer_range: Tuple[float, float] = (-3.14, 3.14)  # radians (±180°)
+
+    head_tilt_actions: torch.Tensor = torch.Tensor([0])
+
     # Classic or Invertible Bicycle dynamics model
     action_space_steer_disc: int = 13
     action_space_accel_disc: int = 7
-    max_steer_angle: float = 1.57  # in radians: pi/2 = 1.57, pi/3 = 1.05
-    max_accel_value: float = 4.0
-    steer_actions: torch.Tensor = torch.round(
-        torch.linspace(
-            -max_steer_angle, max_steer_angle, action_space_steer_disc
-        ),
-        decimals=3,
-    )
-    accel_actions: torch.Tensor = torch.round(
-        torch.linspace(
-            -max_accel_value, max_accel_value, action_space_accel_disc
-        ),
-        decimals=3,
-    )
-    head_tilt_actions: torch.Tensor = torch.Tensor([0])
 
     # Delta Local dynamics model
     dx: torch.Tensor = torch.round(torch.linspace(-2.0, 2.0, 20), decimals=3)
diff --git a/gpudrive/env/env_puffer.py b/gpudrive/env/env_puffer.py
index e083a9ac4..ee4bae1d3 100644
--- a/gpudrive/env/env_puffer.py
+++ b/gpudrive/env/env_puffer.py
@@ -168,8 +168,6 @@ def __init__(
         dynamics_model="classic",
         action_space_steer_disc=13,
         action_space_accel_disc=7,
-        max_steer_angle=1.57,
-        max_accel_value=4.0,
         ego_state=True,
         road_map_obs=True,
         partner_obs=True,
@@ -293,22 +291,8 @@ def __init__(
             lidar_obs=lidar_obs,
             disable_classic_obs=True if lidar_obs else False,
             obs_radius=obs_radius,
-            max_steer_angle=max_steer_angle,
-            max_accel_value=max_accel_value,
             action_space_steer_disc=action_space_steer_disc,
             action_space_accel_disc=action_space_accel_disc,
-            # Override action space
-            steer_actions = torch.round(torch.linspace(
-                    -max_steer_angle, max_steer_angle, action_space_steer_disc
-                ),
-                decimals=3,
-                ),
-            accel_actions = torch.round(
-                torch.linspace(
-                    -max_accel_value, max_accel_value, action_space_accel_disc
-                ),
-                decimals=3,
-            )
         )
         render_config = RenderConfig(
             render_3d=render_3d,
diff --git a/gpudrive/env/env_torch.py b/gpudrive/env/env_torch.py
index 22f2f8b98..d184fa50a 100755
--- a/gpudrive/env/env_torch.py
+++ b/gpudrive/env/env_torch.py
@@ -39,6 +39,12 @@
 import madrona_gpudrive
 
 
+# Agent types
+VEHICLE_AGENT = int(madrona_gpudrive.EntityType.Vehicle)
+CYCLIST_AGENT = int(madrona_gpudrive.EntityType.Cyclist)
+PEDESTRIAN_AGENT = int(madrona_gpudrive.EntityType.Pedestrian)
+
+
 class GPUDriveTorchEnv(GPUDriveGymEnv):
     """Torch Gym Environment that interfaces with the GPU Drive simulator."""
 
@@ -126,7 +132,7 @@ def __init__(
         )
 
         # Action space setup
-        # Action space setup
+        self._cache_agent_types()
         self._setup_action_space(action_type)
         self.single_action_space = self.action_space
         self.num_agents = self.cont_agent_mask.sum().item()
@@ -1028,33 +1034,49 @@ def step_dynamics(self, actions):
 
     def _apply_actions(self, actions):
         """Apply the actions to the simulator."""
-
         if (
             self.config.dynamics_model == "classic"
             or self.config.dynamics_model == "bicycle"
             or self.config.dynamics_model == "delta_local"
         ):
-            if actions.dim() == 2:  # (num_worlds, max_agent_count)
-                # Map action indices to action values if indices are provided
+            if actions.dim() == 2:
                 actions = (
                     torch.nan_to_num(actions, nan=0).long().to(self.device)
                 )
-                self.action_value_tensor = self.action_keys_tensor[actions]
+
+                # Choose mapping based on config
+                if (
+                    self.config.use_type_aware_actions
+                    and self.config.dynamics_model == "classic"
+                ):
+                    self.action_value_tensor = self._map_type_aware_actions(
+                        actions
+                    )
+                else:
+                    self.action_value_tensor = self.action_keys_tensor[actions]
 
             elif actions.dim() == 3:
                 if actions.shape[2] == 1:
                     actions = actions.squeeze(dim=2).to(self.device)
-                    self.action_value_tensor = self.action_keys_tensor[actions]
-                else:  # Assuming we are given the actual action values
+                    if (
+                        self.config.use_type_aware_actions
+                        and self.config.dynamics_model == "classic"
+                    ):
+                        self.action_value_tensor = (
+                            self._map_type_aware_actions(actions)
+                        )
+                    else:
+                        self.action_value_tensor = self.action_keys_tensor[
+                            actions
+                        ]
+                else:
                     self.action_value_tensor = actions.to(self.device)
             else:
                 raise ValueError(f"Invalid action shape: {actions.shape}")
-
         else:
             self.action_value_tensor = actions.to(self.device)
 
         if not hasattr(self, "previous_action_value_tensor"):
-            # Initialize with current actions on first call
             self.previous_action_value_tensor = (
                 self.action_value_tensor.clone()
             )
@@ -1068,14 +1090,29 @@ def _apply_actions(self, actions):
                 self.action_value_tensor.clone()
             )
 
-        # Calculate action differences (jerk)
         self.action_diff = (
             self.action_value_tensor - self.previous_action_value_tensor
         )
         self.previous_action_value_tensor = self.action_value_tensor.clone()
-
         self._copy_actions_to_simulator(self.action_value_tensor)
 
+    def _map_type_aware_actions(self, action_indices):
+        """Map action indices to type-specific action values."""
+        action_values = self.action_keys_tensor[
+            action_indices
+        ]  # Start with default (vehicle ranges)
+
+        # Override with type-specific actions
+        for agent_type in [CYCLIST_AGENT, PEDESTRIAN_AGENT]:
+            type_mask = self.agent_types == agent_type
+            if torch.any(type_mask):
+                type_action_tensor = self.type_action_tensors[agent_type]
+                action_values[type_mask] = type_action_tensor[
+                    action_indices[type_mask]
+                ]
+
+        return action_values
+
     def _copy_actions_to_simulator(self, actions):
         """Copy the provided actions to the simulator."""
         if (
@@ -1110,25 +1147,46 @@ def _set_discrete_action_space(self) -> None:
             self.config.dynamics_model == "classic"
             or self.config.dynamics_model == "bicycle"
         ):
-            self.steer_actions = self.config.steer_actions.to(self.device)
-            self.accel_actions = self.config.accel_actions.to(self.device)
+
+            # Use vehicle ranges for the base action space
+            self.steer_actions = torch.round(
+                torch.linspace(
+                    self.config.vehicle_steer_range[0],
+                    self.config.vehicle_steer_range[1],
+                    self.config.action_space_steer_disc,
+                ),
+                decimals=3,
+            ).to(self.device)
+            self.accel_actions = torch.round(
+                torch.linspace(
+                    self.config.vehicle_accel_range[0],
+                    self.config.vehicle_accel_range[1],
+                    self.config.action_space_accel_disc,
+                ),
+                decimals=3,
+            ).to(self.device)
             self.head_actions = self.config.head_tilt_actions.to(self.device)
+
             products = product(
                 self.accel_actions, self.steer_actions, self.head_actions
             )
+
+            # If type-aware actions are enabled, setup the type-specific mappings
+            if self.config.use_type_aware_actions:
+                self._setup_type_aware_mappings()
+
         elif self.config.dynamics_model == "state":
             self.x = self.config.x.to(self.device)
             self.y = self.config.y.to(self.device)
             self.yaw = self.config.yaw.to(self.device)
             self.vx = self.config.vx.to(self.device)
             self.vy = self.config.vy.to(self.device)
-
         else:
             raise ValueError(
                 f"Invalid dynamics model: {self.config.dynamics_model}"
             )
 
-        # Create a mapping from action indices to action values
+        # Create standard action mapping (same for both cases)
         self.action_key_to_values = {}
         self.values_to_action_key = {}
         if products is not None:
@@ -1153,39 +1211,56 @@ def _set_discrete_action_space(self) -> None:
                 ]
             ).to(self.device)
 
-            return Discrete(n=int(len(self.action_key_to_values)))
+            self.action_space = Discrete(n=int(len(self.action_key_to_values)))
+            return self.action_space
         else:
-            return Discrete(n=1)
+            self.action_space = Discrete(n=1)
+            return self.action_space
+
+    def _setup_type_aware_mappings(self):
+        """Setup type-specific action mappings."""
+        steer_disc = self.config.action_space_steer_disc
+        accel_disc = self.config.action_space_accel_disc
+
+        # Create type-specific action ranges
+        type_ranges = {
+            VEHICLE_AGENT: (
+                self.config.vehicle_accel_range,
+                self.config.vehicle_steer_range,
+            ),  # Vehicle
+            CYCLIST_AGENT: (
+                self.config.cyclist_accel_range,
+                self.config.cyclist_steer_range,
+            ),  # Cyclist
+            PEDESTRIAN_AGENT: (
+                self.config.pedestrian_accel_range,
+                self.config.pedestrian_steer_range,
+            ),  # Pedestrian
+        }
 
-    def _set_continuous_action_space(self) -> None:
-        """Configure the continuous action space."""
-        if self.config.dynamics_model == "delta_local":
-            self.dx = self.config.dx.to(self.device)
-            self.dy = self.config.dy.to(self.device)
-            self.dyaw = self.config.dyaw.to(self.device)
-            action_1 = self.dx.clone().cpu().numpy()
-            action_2 = self.dy.clone().cpu().numpy()
-            action_3 = self.dyaw.clone().cpu().numpy()
-        elif self.config.dynamics_model == "classic":
-            self.steer_actions = self.config.steer_actions.to(self.device)
-            self.accel_actions = self.config.accel_actions.to(self.device)
-            self.head_actions = torch.tensor([0], device=self.device)
-            action_1 = self.steer_actions.clone().cpu().numpy()
-            action_2 = self.accel_actions.clone().cpu().numpy()
-            action_3 = self.head_actions.clone().cpu().numpy()
-        else:
-            raise ValueError(
-                f"Continuous action space is currently not supported for dynamics_model: {self.config.dynamics_model}."
+        self.type_action_tensors = {}
+
+        for agent_type, (accel_range, steer_range) in type_ranges.items():
+            # Create discrete actions for this agent type
+            accel_values = torch.linspace(
+                accel_range[0], accel_range[1], accel_disc, device=self.device
+            )
+            steer_values = torch.linspace(
+                steer_range[0], steer_range[1], steer_disc, device=self.device
             )
 
-        action_space = Tuple(
-            (
-                Box(action_1.min(), action_1.max(), shape=(1,)),
-                Box(action_2.min(), action_2.max(), shape=(1,)),
-                Box(action_3.min(), action_3.max(), shape=(1,)),
+            # Create action tensor for this type
+            type_products = list(
+                product(accel_values, steer_values, self.head_actions)
             )
-        )
-        return action_space
+            self.type_action_tensors[agent_type] = torch.tensor(
+                [
+                    [accel.item(), steer.item(), head.item()]
+                    for accel, steer, head in type_products
+                ],
+                device=self.device,
+            )
+
 
     def _get_guidance(self, mask=None) -> torch.Tensor:
         """Receive (expert) suggestions from pre-trained model or logs."""
@@ -1633,7 +1708,9 @@ def _get_bev_obs(self, mask=None):
             return bev.bev_segmentation_map.flatten(start_dim=2)
 
     def get_obs(self, mask=None):
-        """Get observation: Combine different types of environment information into a single tensor.
+        """
+        Get observation: Combine different types of environment information
+        into a single tensor.
         Returns:
             torch.Tensor: (num_worlds, max_agent_count, num_features)
         """
@@ -1753,9 +1830,11 @@ def swap_data_batch(self, data_batch=None):
             controlled_agent_mask=self.cont_agent_mask,
             reference_trajectory=self.reference_trajectory,
         )
-        
+
         self.guidance_dropout_mask = self.create_guidance_dropout_mask()
 
+        self._cache_agent_types()
+
     def get_expert_actions(self):
         """Get expert actions for the full trajectories across worlds.
 
@@ -1822,6 +1901,101 @@ def get_expert_actions(self):
             log_trajectory.yaw,
         )
 
+    def _cache_agent_types(self):
+        """Cache agent types from info tensor."""
+        info_tensor = self.sim.info_tensor().to_torch()
+        self.agent_types = info_tensor[
+            :, :, 4
+        ].long()  # Shape: [num_worlds, max_agents]
+
+    def _setup_type_aware_actions(self):
+        """Setup type-aware action mappings using predefined physical ranges."""
+        # Create action tensors for each agent type using same discrete counts
+        self.type_action_tensors = {}
+
+        steer_disc = self.config.action_space_steer_disc
+        accel_disc = self.config.action_space_accel_disc
+
+        # Vehicle actions (type 0)
+        vehicle_accel = torch.linspace(
+            self.config.vehicle_accel_range[0],
+            self.config.vehicle_accel_range[1],
+            accel_disc,
+            device=self.device,
+        )
+        vehicle_steer = torch.linspace(
+            self.config.vehicle_steer_range[0],
+            self.config.vehicle_steer_range[1],
+            steer_disc,
+            device=self.device,
+        )
+
+        # Cyclist actions (type 1)
+        cyclist_accel = torch.linspace(
+            self.config.cyclist_accel_range[0],
+            self.config.cyclist_accel_range[1],
+            accel_disc,
+            device=self.device,
+        )
+        cyclist_steer = torch.linspace(
+            self.config.cyclist_steer_range[0],
+            self.config.cyclist_steer_range[1],
+            steer_disc,
+            device=self.device,
+        )
+
+        # Pedestrian actions (type 2)
+        pedestrian_accel = torch.linspace(
+            self.config.pedestrian_accel_range[0],
+            self.config.pedestrian_accel_range[1],
+            accel_disc,
+            device=self.device,
+        )
+        pedestrian_steer = torch.linspace(
+            self.config.pedestrian_steer_range[0],
+            self.config.pedestrian_steer_range[1],
+            steer_disc,
+            device=self.device,
+        )
+
+        # Vehicle action tensor
+        vehicle_products = list(
+            product(vehicle_accel, vehicle_steer, self.head_actions)
+        )
+        self.type_action_tensors[0] = torch.tensor(
+            [
+                [accel.item(), steer.item(), head.item()]
+                for accel, steer, head in vehicle_products
+            ],
+            device=self.device,
+        )
+
+        # Cyclist action tensor
+        cyclist_products = list(
+            product(cyclist_accel, cyclist_steer, self.head_actions)
+        )
+        self.type_action_tensors[1] = torch.tensor(
+            [
+                [accel.item(), steer.item(), head.item()]
+                for accel, steer, head in cyclist_products
+            ],
+            device=self.device,
+        )
+
+        # Pedestrian action tensor
+        pedestrian_products = list(
+            product(pedestrian_accel, pedestrian_steer, self.head_actions)
+        )
+        self.type_action_tensors[2] = torch.tensor(
+            [
+                [accel.item(), steer.item(), head.item()]
+                for accel, steer, head in pedestrian_products
+            ],
+            device=self.device,
+        )
+
+        self._cache_agent_types()
+
     def get_env_filenames(self):
         """Obtain the tfrecord filename for each world, mapping world indices to map names."""
 
@@ -1889,7 +2063,7 @@ def render(self, focus_env_idx=0, focus_agent_idx=[0, 1]):
         add_reference_heading=False,
         reward_type="guided_autonomy",
         init_mode="wosac_train",
-        dynamics_model="delta_local",  # "state", #"classic",
+        dynamics_model="classic",  # "state", #"classic",
         smoothen_trajectory=False,
         add_previous_action=True,
         guidance_dropout_prob=0.9,  # 0.95,
@@ -1898,8 +2072,8 @@ def render(self, focus_env_idx=0, focus_agent_idx=[0, 1]):
 
     # Create data loader
     train_loader = SceneDataLoader(
-        root="data/processed/validation",
-        batch_size=1,
+        root="data/processed/wosac/validation_interactive/json",
+        batch_size=10,
         dataset_size=100,
         sample_with_replacement=False,
         shuffle=False,
@@ -1930,8 +2104,8 @@ def render(self, focus_env_idx=0, focus_agent_idx=[0, 1]):
         print(f"Step: {env.step_in_world[0, 0, 0].item()}")
 
         # Step the environment
-        expert_actions, _, _, _ = env.get_expert_actions()
-        env.step_dynamics(expert_actions[:, :, time_step, :])
+        # env.step_dynamics(expert_actions[:, :, time_step, :])
+        env.step_dynamics(torch.zeros_like(control_mask))
 
         obs = env.get_obs(control_mask)
         reward = env.get_rewards()
diff --git a/gpudrive/visualize/core.py b/gpudrive/visualize/core.py
index 2cebb1312..4dab953e2 100644
--- a/gpudrive/visualize/core.py
+++ b/gpudrive/visualize/core.py
@@ -2027,14 +2027,14 @@ def plot_agent_observation(
         ax.add_patch(observation_circle)
 
         # Add V lines for view cone boundaries
-        x1 = self.env_config.obs_radius * np.cos(self.env_config.view_cone_half_angle)
-        y1 = self.env_config.obs_radius * np.sin(self.env_config.view_cone_half_angle)
+        # x1 = self.env_config.obs_radius * np.cos(self.env_config.view_cone_half_angle)
+        # y1 = self.env_config.obs_radius * np.sin(self.env_config.view_cone_half_angle)
 
-        x2 = self.env_config.obs_radius * np.cos(-self.env_config.view_cone_half_angle)
-        y2 = self.env_config.obs_radius * np.sin(-self.env_config.view_cone_half_angle)
+        # x2 = self.env_config.obs_radius * np.cos(-self.env_config.view_cone_half_angle)
+        # y2 = self.env_config.obs_radius * np.sin(-self.env_config.view_cone_half_angle)
 
-        ax.add_line(Line2D([0, x1], [0, y1], color="#FF9900", linewidth=1.5, linestyle="--"))
-        ax.add_line(Line2D([0, x2], [0, y2], color="#FF9900", linewidth=1.5, linestyle="--"))
+        # ax.add_line(Line2D([0, x1], [0, y1], color="#FF9900", linewidth=1.5, linestyle="--"))
+        # ax.add_line(Line2D([0, x2], [0, y2], color="#FF9900", linewidth=1.5, linestyle="--"))
 
         ax.set_xticks([])
         ax.set_yticks([])

From 60081e254c325a0ff005272a162d32e330ef70e6 Mon Sep 17 00:00:00 2001
From: Robin <49201699+rjs02@users.noreply.github.com>
Date: Thu, 5 Jun 2025 00:40:39 +0200
Subject: [PATCH 24/38] Fix out of range error for VBD trajectory (#456)

* fix empty geometry handling and out of bounds indices

* add safer access and overflow prevention

* allow flexible no of elements
---
 src/json_serialization.hpp | 230 ++++++++++++++++++++++++-------------
 1 file changed, 150 insertions(+), 80 deletions(-)

diff --git a/src/json_serialization.hpp b/src/json_serialization.hpp
index 73ad38551..514acf7e5 100644
--- a/src/json_serialization.hpp
+++ b/src/json_serialization.hpp
@@ -31,90 +31,128 @@ namespace madrona_gpudrive
 
         float zPositions[10];
 
-        for (const auto &pos : j.at("position"))
-        {
-            if (i < MAX_POSITIONS)
+        // Check if position array exists and process it
+        if (j.contains("position") && j.at("position").is_array()) {
+            for (const auto &pos : j.at("position"))
             {
-                from_json(pos, obj.position[i]);
-                obj.mean.x += (obj.position[i].x - obj.mean.x)/(i+1);
-                obj.mean.y += (obj.position[i].y - obj.mean.y)/(i+1);
+                if (i < MAX_POSITIONS)
+                {
+                    from_json(pos, obj.position[i]);
+                    obj.mean.x += (obj.position[i].x - obj.mean.x)/(i+1);
+                    obj.mean.y += (obj.position[i].y - obj.mean.y)/(i+1);
 
-                // Store z position separately
-                if (i < 10)
+                    // Store z position separately
+                    if (i < 10)
+                    {
+                        float z = extract_z_from_position(pos);
+                        zPositions[i] = z;
+                    }
+
+                    ++i;
+                }
+                else
                 {
-                    float z = extract_z_from_position(pos);
-                    zPositions[i] = z;
+                    break; // Avoid overflow
                 }
-
-                ++i;
-            }
-            else
-            {
-                break; // Avoid overflow
             }
         }
         obj.numPositions = i;
-        j.at("width").get_to(obj.vehicle_size.width);
-        j.at("length").get_to(obj.vehicle_size.length);
-        j.at("height").get_to(obj.vehicle_size.height);
-        j.at("id").get_to(obj.id);
+        
+        // Check if required fields exist before accessing
+        if (j.contains("width")) {
+            j.at("width").get_to(obj.vehicle_size.width);
+        } else {
+            obj.vehicle_size.width = 0.0f;
+        }
+        
+        if (j.contains("length")) {
+            j.at("length").get_to(obj.vehicle_size.length);
+        } else {
+            obj.vehicle_size.length = 0.0f;
+        }
+        
+        if (j.contains("height")) {
+            j.at("height").get_to(obj.vehicle_size.height);
+        } else {
+            obj.vehicle_size.height = 0.0f;
+        }
+        
+        if (j.contains("id")) {
+            j.at("id").get_to(obj.id);
+        } else {
+            obj.id = 0;
+        }
 
         i = 0;
-        for (const auto &h : j.at("heading"))
-        {
-            if (i < MAX_POSITIONS)
+        if (j.contains("heading") && j.at("heading").is_array()) {
+            for (const auto &h : j.at("heading"))
             {
-                h.get_to(obj.heading[i]);
-                ++i;
-            }
-            else
-            {
-                break; // Avoid overflow
+                if (i < MAX_POSITIONS)
+                {
+                    h.get_to(obj.heading[i]);
+                    ++i;
+                }
+                else
+                {
+                    break; // Avoid overflow
+                }
             }
         }
         obj.numHeadings = i;
 
         i = 0;
-        for (const auto &v : j.at("velocity"))
-        {
-            if (i < MAX_POSITIONS)
-            {
-                from_json(v, obj.velocity[i]);
-                ++i;
-            }
-            else
+        if (j.contains("velocity") && j.at("velocity").is_array()) {
+            for (const auto &v : j.at("velocity"))
             {
-                break; // Avoid overflow
+                if (i < MAX_POSITIONS)
+                {
+                    from_json(v, obj.velocity[i]);
+                    ++i;
+                }
+                else
+                {
+                    break; // Avoid overflow
+                }
             }
         }
         obj.numVelocities = i;
 
         i = 0;
-        for (const auto &v : j.at("valid"))
-        {
-            if (i < MAX_POSITIONS)
-            {
-                v.get_to(obj.valid[i]);
-                ++i;
-            }
-            else
+        if (j.contains("valid") && j.at("valid").is_array()) {
+            for (const auto &v : j.at("valid"))
             {
-                break; // Avoid overflow
+                if (i < MAX_POSITIONS)
+                {
+                    v.get_to(obj.valid[i]);
+                    ++i;
+                }
+                else
+                {
+                    break; // Avoid overflow
+                }
             }
         }
         obj.numValid = i;
 
-
-        from_json(j.at("goalPosition"), obj.goalPosition);
-        std::string type = j.at("type");
-        if(type == "vehicle")
-            obj.type = EntityType::Vehicle;
-        else if(type == "pedestrian")
-            obj.type = EntityType::Pedestrian;
-        else if(type == "cyclist")
-            obj.type = EntityType::Cyclist;
-        else
+        if (j.contains("goalPosition")) {
+            from_json(j.at("goalPosition"), obj.goalPosition);
+        } else {
+            obj.goalPosition = {0.0f, 0.0f};
+        }
+        
+        if (j.contains("type")) {
+            std::string type = j.at("type");
+            if(type == "vehicle")
+                obj.type = EntityType::Vehicle;
+            else if(type == "pedestrian")
+                obj.type = EntityType::Pedestrian;
+            else if(type == "cyclist")
+                obj.type = EntityType::Cyclist;
+            else
+                obj.type = EntityType::None;
+        } else {
             obj.type = EntityType::None;
+        }
 
         std::string markAsExpertKey = "mark_as_expert";
         if (j.contains(markAsExpertKey)) {
@@ -128,9 +166,9 @@ namespace madrona_gpudrive
         // Store in the avgZ field of map object
         float avgZ = 0.0f;
         float avg_count = 0.0f;
-        for (int zi = 0; zi < 10; zi++) {
+        for (int zi = 0; zi < 10 && zi < obj.numValid; zi++) {
             // Only consider valid z positions
-            if (obj.valid[zi]) {
+            if (zi < obj.numValid && obj.valid[zi]) {
                 avgZ += zPositions[zi];
                 avg_count += 1;
             }
@@ -152,17 +190,18 @@ namespace madrona_gpudrive
         }
 
         // If VBD trajectories exist in the JSON, read them
-        if (j.contains("vbd_trajectory")) {
+        if (j.contains("vbd_trajectory") && j.at("vbd_trajectory").is_array()) {
             int vbd_idx = 0;
             for (const auto &vbd_traj : j.at("vbd_trajectory")) {
                 if (vbd_idx < consts::kTrajectoryLength) {
-                    if (!vbd_traj.is_null()) {
-                        obj.vbd_trajectories[vbd_idx][0] = vbd_traj.at(0).get<float>();
-                        obj.vbd_trajectories[vbd_idx][1] = vbd_traj.at(1).get<float>();
-                        obj.vbd_trajectories[vbd_idx][2] = vbd_traj.at(2).get<float>();
-                        obj.vbd_trajectories[vbd_idx][3] = vbd_traj.at(3).get<float>();
-                        obj.vbd_trajectories[vbd_idx][4] = vbd_traj.at(4).get<float>();
-                        obj.vbd_trajectories[vbd_idx][5] = vbd_traj.at(5).get<float>();
+                    if (!vbd_traj.is_null() && vbd_traj.is_array()) {
+                        size_t traj_size = vbd_traj.size();
+                        if (traj_size > 0) obj.vbd_trajectories[vbd_idx][0] = vbd_traj.at(0).get<float>();
+                        if (traj_size > 1) obj.vbd_trajectories[vbd_idx][1] = vbd_traj.at(1).get<float>();
+                        if (traj_size > 2) obj.vbd_trajectories[vbd_idx][2] = vbd_traj.at(2).get<float>();
+                        if (traj_size > 3) obj.vbd_trajectories[vbd_idx][3] = vbd_traj.at(3).get<float>();
+                        if (traj_size > 4) obj.vbd_trajectories[vbd_idx][4] = vbd_traj.at(4).get<float>();
+                        if (traj_size > 5) obj.vbd_trajectories[vbd_idx][5] = vbd_traj.at(5).get<float>();
                     }
                 }
                 vbd_idx++;
@@ -189,6 +228,11 @@ namespace madrona_gpudrive
         else
             road.type = EntityType::None;
 
+        // Check if geometry exists and is not empty
+        if (!j.contains("geometry") || j.at("geometry").empty()) {
+            road.numPoints = 0;
+            return;
+        }
 
         std::vector<MapVector2> geometry_points_;
         for(const auto &point: j.at("geometry"))
@@ -198,9 +242,16 @@ namespace madrona_gpudrive
             geometry_points_.push_back(p);
         }
 
-        const int64_t num_segments = j["geometry"].size() - 1;
+        const int64_t geometry_size = static_cast<int64_t>(geometry_points_.size());
+        if (geometry_size == 0) {
+            road.numPoints = 0;
+            return;
+        }
+
+        const int64_t num_segments = geometry_size - 1;
         const int64_t sample_every_n_ = 1;
         const int64_t num_sampled_points = (num_segments + sample_every_n_ - 1) / sample_every_n_ + 1;
+        
         if (num_segments >= 10 && (road.type == EntityType::RoadLane || road.type == EntityType::RoadEdge || road.type == EntityType::RoadLine))
         {
             std::vector<bool> skip(num_sampled_points, false); // This list tracks the points that are skipped
@@ -226,9 +277,19 @@ namespace madrona_gpudrive
                     }
                     if (k_2 >= num_sampled_points)
                         break;
-                    auto point1 = geometry_points_[k * sample_every_n_];
-                    auto point2 = geometry_points_[k_1 * sample_every_n_];
-                    auto point3 = geometry_points_[k_2 * sample_every_n_];
+                    
+                    // Add bounds checking for geometry_points_ access
+                    int64_t idx1 = k * sample_every_n_;
+                    int64_t idx2 = k_1 * sample_every_n_;
+                    int64_t idx3 = k_2 * sample_every_n_;
+                    
+                    if (idx1 >= geometry_size || idx2 >= geometry_size || idx3 >= geometry_size) {
+                        break;
+                    }
+                    
+                    auto point1 = geometry_points_[idx1];
+                    auto point2 = geometry_points_[idx2];
+                    auto point3 = geometry_points_[idx3];
                     float_t area = 0.5 * std::abs((point1.x - point3.x) * (point2.y - point1.y) - (point1.x - point2.x) * (point3.y - point1.y));
                     if (area < polylineReductionThreshold)
                     {                       // If the area is less than the threshold, then we skip the middle point
@@ -245,34 +306,43 @@ namespace madrona_gpudrive
 
             // Create the road lines
             k = 0;
-            skip[0] = false;
-            skip[num_sampled_points - 1] = false;
+            if (num_sampled_points > 0) {
+                skip[0] = false;
+            }
+            if (num_sampled_points > 1) {
+                skip[num_sampled_points - 1] = false;
+            }
             std::vector<MapVector2> new_geometry_points; // This list stores the points that are not skipped
             while (k < num_sampled_points)
             {
-                if (!skip[k])
+                int64_t idx = k * sample_every_n_;
+                if (idx < geometry_size && !skip[k])
                 {
-                    new_geometry_points.push_back(geometry_points_[k * sample_every_n_]); // Add the point to the list if it is not skipped
+                    new_geometry_points.push_back(geometry_points_[idx]); // Add the point to the list if it is not skipped
                 }
                 k++;
             }
             for (size_t i = 0; i < new_geometry_points.size(); i++)
             {
-                if(i==MAX_GEOMETRY)
+                if(i >= MAX_GEOMETRY)
                     break;
                 road.geometry[i] = new_geometry_points[i]; // Create the road lines
             }
-            road.numPoints = new_geometry_points.size();
+            road.numPoints = std::min(new_geometry_points.size(), static_cast<size_t>(MAX_GEOMETRY));
         }
         else
         {
             for (int64_t i = 0; i < num_sampled_points ; ++i)
             {
-                if(i==MAX_GEOMETRY)
+                if(i >= MAX_GEOMETRY)
+                    break;
+                int64_t idx = i * sample_every_n_;
+                if (idx >= geometry_size) {
                     break;
-                road.geometry[i] = geometry_points_[i * sample_every_n_];
+                }
+                road.geometry[i] = geometry_points_[idx];
             }
-            road.numPoints = num_sampled_points;
+            road.numPoints = std::min(static_cast<size_t>(num_sampled_points), static_cast<size_t>(MAX_GEOMETRY));
         }
 
         if (j.contains("id")) {

From bc6286801e242e9dbe2e21cfb5acd38f05ca2a1f Mon Sep 17 00:00:00 2001
From: Robin Sieber <rjs02@hotmail.com>
Date: Mon, 2 Jun 2025 14:04:59 +0200
Subject: [PATCH 25/38] simple occlusion check working

---
 gpudrive/env/base_env.py |   1 +
 gpudrive/env/config.py   |   3 +-
 src/bindings.cpp         |   1 +
 src/init.hpp             |   1 +
 src/sim.cpp              | 165 +++++++++++++++++++++++++++++++++------
 5 files changed, 147 insertions(+), 24 deletions(-)

diff --git a/gpudrive/env/base_env.py b/gpudrive/env/base_env.py
index 1676fa4e7..7034a766d 100755
--- a/gpudrive/env/base_env.py
+++ b/gpudrive/env/base_env.py
@@ -87,6 +87,7 @@ def _set_road_reduction_params(self, params):
         """
         params.observationRadius = self.config.obs_radius
         params.viewConeHalfAngle = self.config.view_cone_half_angle
+        params.viewOccludeObjects = self.config.view_occlude_objects
         if self.config.road_obs_algorithm == "k_nearest_roadpoints":
             params.roadObservationAlgorithm = (
                 madrona_gpudrive.FindRoadObservationsWith.KNearestEntitiesWithRadiusFiltering
diff --git a/gpudrive/env/config.py b/gpudrive/env/config.py
index 9300c21d6..d0f1d8bee 100755
--- a/gpudrive/env/config.py
+++ b/gpudrive/env/config.py
@@ -57,7 +57,8 @@ class EnvConfig:
     # Road observation algorithm settings
     road_obs_algorithm: str = "linear"  # Algorithm for road observations
     obs_radius: float = 50.0  # Radius for road observations
-    view_cone_half_angle: float = torch.pi  # Half-angle for view cone setting
+    view_cone_half_angle: float = torch.pi / 1.5  # Half-angle for view cone setting
+    view_occlude_objects: bool = False  # True: Vehicles are removed from observations if they are occluded by other vehicles
     polyline_reduction_threshold: float = (
         0.1  # Threshold for polyline reduction
     )
diff --git a/src/bindings.cpp b/src/bindings.cpp
index e5b5a1af0..9dcf17025 100755
--- a/src/bindings.cpp
+++ b/src/bindings.cpp
@@ -51,6 +51,7 @@ namespace madrona_gpudrive
             .def_rw("polylineReductionThreshold", &Parameters::polylineReductionThreshold)
             .def_rw("observationRadius", &Parameters::observationRadius)
             .def_rw("viewConeHalfAngle", &Parameters::viewConeHalfAngle)
+            .def_rw("viewOccludeObjects", &Parameters::viewOccludeObjects)
             .def_rw("rewardParams", &Parameters::rewardParams)
             .def_rw("collisionBehaviour", &Parameters::collisionBehaviour)
             .def_rw("goalBehaviour", &Parameters::goalBehaviour)
diff --git a/src/init.hpp b/src/init.hpp
index 5eb6a8ece..c72522bec 100755
--- a/src/init.hpp
+++ b/src/init.hpp
@@ -120,6 +120,7 @@ namespace madrona_gpudrive
         float polylineReductionThreshold;
         float observationRadius;
         float viewConeHalfAngle;
+        bool viewOccludeObjects;
         RewardParams rewardParams;
         CollisionBehaviour collisionBehaviour = CollisionBehaviour::AgentStop; // Default: AgentStop
         GoalBehaviour goalBehaviour = GoalBehaviour::Remove;  // Default to current behavior
diff --git a/src/sim.cpp b/src/sim.cpp
index 7a1a6a6e2..2ce493985 100755
--- a/src/sim.cpp
+++ b/src/sim.cpp
@@ -196,6 +196,102 @@ inline void collectSelfObsSystem(Engine &ctx,
     self_obs.steerAngle = vel.angular.z;
 }
 
+struct ViewField {
+    const Position& position;
+    const float heading;
+    const float viewRadius;
+    const float viewAngle;
+
+    ViewField(const Position& position, const float heading, const float viewRadius, const float viewAngle)
+        : position(position), heading(heading), viewRadius(viewRadius), viewAngle(viewAngle) {}
+};
+
+// Broad-phase filtering: Get dynamic objects that are potentially visible within the view field
+inline std::vector<Entity> visibleCandidatesDynamic(Engine &ctx, 
+                                                    const ViewField &vf,
+                                                    const OtherAgents &other_agents,
+                                                    Entity self_entity) {
+    std::vector<Entity> candidates;
+    
+    // Simple radius-based broad phase filtering for now
+    for (CountT agentIdx = 0; agentIdx < ctx.data().numAgents - 1; ++agentIdx) {
+        Entity other = other_agents.e[agentIdx];
+        if (other == self_entity) continue; // Don't include self
+        
+        const Position &other_pos = ctx.get<Position>(other);
+        Vector2 relative_pos = (other_pos - vf.position).xy();
+        
+        // Check if within view radius
+        if (relative_pos.length() <= vf.viewRadius) {
+            // Check if within view cone
+            Vector2 forward_dir = {cosf(vf.heading), sinf(vf.heading)};
+            Vector2 normalized_relative_pos = relative_pos / relative_pos.length();
+            float dot_product = normalized_relative_pos.dot(forward_dir);
+            float angle = acosf(fmaxf(-1.0f, fminf(1.0f, dot_product))); // clamp to [-1, 1]
+            
+            if (angle <= vf.viewAngle * 0.5f) {
+                candidates.push_back(other);
+            }
+        }
+    }
+    
+    return candidates;
+}
+
+// Broad-phase filtering: Get static road objects that are potentially visible within the view field
+inline std::vector<Entity> visibleCandidatesStatic(Engine &ctx,
+                                                   const ViewField &vf) {
+    std::vector<Entity> candidates;
+    
+    // Simple radius-based broad phase filtering for roads
+    for (CountT roadIdx = 0; roadIdx < ctx.data().numRoads; ++roadIdx) {
+        Entity road = ctx.data().roads[roadIdx];
+        const Position &road_pos = ctx.get<Position>(road);
+        Vector2 relative_pos = (road_pos - vf.position).xy();
+        
+        // Check if within view radius
+        if (relative_pos.length() <= vf.viewRadius) {
+            // Check if within view cone
+            Vector2 forward_dir = {cosf(vf.heading), sinf(vf.heading)};
+            Vector2 normalized_relative_pos = relative_pos / relative_pos.length();
+            float dot_product = normalized_relative_pos.dot(forward_dir);
+            float angle = acosf(fmaxf(-1.0f, fminf(1.0f, dot_product))); // clamp to [-1, 1]
+            
+            if (angle <= vf.viewAngle * 0.5f) {
+                candidates.push_back(road);
+            }
+        }
+    }
+    
+    return candidates;
+}
+
+// Narrow-phase filtering: Check for occlusion using simple ray casting
+inline void filterByOcclusion(Engine &ctx,
+                              const ViewField &vf,
+                              std::vector<Entity> &objects) {
+    auto &bvh = ctx.singleton<broadphase::BVH>();
+    Vector3 observer_pos = {vf.position.x, vf.position.y, vf.position.z};
+    
+    // Filter out occluded objects
+    objects.erase(std::remove_if(objects.begin(), objects.end(), 
+        [&](Entity target) {
+            const Position &target_pos = ctx.get<Position>(target);
+            Vector3 target_pos_3d = {target_pos.x, target_pos.y, target_pos.z};
+            
+            Vector3 ray_dir = (target_pos_3d - observer_pos).normalize();
+            float target_distance = (target_pos_3d - observer_pos).length();
+            
+            // Trace ray from observer to target
+            float hit_t;
+            Vector3 hit_normal;
+            Entity hit_entity = bvh.traceRay(observer_pos, ray_dir, &hit_t, &hit_normal, target_distance - 0.1f);
+            
+            // If we hit something before reaching the target, it's occluded
+            return (hit_entity != Entity::none() && hit_entity != target);
+        }), objects.end());
+}
+
 inline void collectPartnerObsSystem(Engine &ctx,
                               const Position &pos,
                               const Rotation &rot,
@@ -205,13 +301,33 @@ inline void collectPartnerObsSystem(Engine &ctx,
     if(ctx.data().params.disableClassicalObs)
         return;
 
+    // User config
+    // viewRadius (float), viewConeHalfAngle (float), viewOccludeObjects (bool; False = no filtering), 
+
+    // to write partner features into array
     auto &partner_obs = ctx.get<PartnerObservations>(agent_iface.e);
 
-    CountT arrIndex = 0; CountT agentIdx = 0;
-    while(agentIdx < ctx.data().numAgents - 1)
-    {
-        Entity other = other_agents.e[agentIdx++];
+    // Get agent's heading from rotation
+    float heading = utils::quatToYaw(rot);
+    float viewRadius = ctx.data().params.observationRadius;
+    float viewAngle = ctx.data().params.viewConeHalfAngle * 2.0f; // Convert half-angle to full angle
+    
+    ViewField vf(pos, heading, viewRadius, viewAngle);
+
+    // broad phase: remove if not in view field + remove self
+    auto objectsDynamic = visibleCandidatesDynamic(ctx, vf, other_agents, agent_iface.e);
+        
+    // narrow phase: raycast to get occlusion by other dynamic objects
+    if (ctx.data().params.viewOccludeObjects) {
+        filterByOcclusion(ctx, vf, objectsDynamic);
+    }
 
+    // add observations to array partner_obs.obs based on filtered list
+    CountT arrIndex = 0;
+    for (Entity other : objectsDynamic) {
+        // future optimization: k nearest maybe in case of many agents in view
+        if (arrIndex >= consts::kMaxAgentCount - 1) break;
+        
         const Position &other_position = ctx.get<Position>(other);
         const Velocity &other_velocity = ctx.get<Velocity>(other);
         const Rotation &other_rot = ctx.get<Rotation>(other);
@@ -222,23 +338,8 @@ inline void collectPartnerObsSystem(Engine &ctx,
         float relative_speed = other_velocity.linear.length(); // Design decision: return the speed of the other agent directly
 
         Rotation relative_orientation = rot.inv() * other_rot;
-
         float relative_heading = utils::quatToYaw(relative_orientation);
 
-        if(relative_pos.length() > ctx.data().params.observationRadius)
-        {
-            continue;
-        }
-
-        float cos_angle = relative_pos.x/relative_pos.length();
-        float angle = std::acos(cos_angle);
-
-        // Only include agents that are within the view cone
-        if(angle > ctx.data().params.viewConeHalfAngle)
-        {
-            continue;
-        }
-
         partner_obs.obs[arrIndex++] = {
             .speed = relative_speed,
             .position = relative_pos,
@@ -248,6 +349,8 @@ inline void collectPartnerObsSystem(Engine &ctx,
             .id = (float)ctx.get<AgentID>(ctx.get<AgentInterfaceEntity>(other).e).id
         };
     }
+    
+    // Zero out remaining entries
     while(arrIndex < consts::kMaxAgentCount - 1) {
         partner_obs.obs[arrIndex++] = PartnerObservation::zero();
     }
@@ -263,6 +366,17 @@ inline void collectMapObservationsSystem(Engine &ctx,
 
     auto &map_obs = ctx.get<AgentMapObservations>(agent_iface.e);
 
+    // User config (some of it not implemented yet, but assume it will be): 
+    // viewRadius (float), viewConeHalfAngle (float), viewOccludeObjects (bool; False = no filtering), 
+
+    // Get agent's heading from rotation
+    float heading = utils::quatToYaw(rot);
+    float view_radius = ctx.data().params.observationRadius;
+    float view_angle = ctx.data().params.viewConeHalfAngle * 2.0f;
+    
+    ViewField vf(pos, heading, view_radius, view_angle);
+    auto objectsStatic = visibleCandidatesStatic(ctx, vf);
+
     const auto alg = ctx.data().params.roadObservationAlgorithm;
     if (alg == FindRoadObservationsWith::KNearestEntitiesWithRadiusFiltering) {
         selectKNearestRoadEntities<consts::kMaxAgentMapObservationsCount>(
@@ -273,9 +387,11 @@ inline void collectMapObservationsSystem(Engine &ctx,
     assert(alg == FindRoadObservationsWith::AllEntitiesWithRadiusFiltering);
 
     utils::ReferenceFrame referenceFrame(pos.xy(), rot);
-    CountT arrIndex = 0; CountT roadIdx = 0;
-    while(roadIdx < ctx.data().numRoads && arrIndex < consts::kMaxAgentMapObservationsCount) {
-        Entity road = ctx.data().roads[roadIdx++];
+    CountT arrIndex = 0;
+    
+    for (Entity road : objectsStatic) {
+        if (arrIndex >= consts::kMaxAgentMapObservationsCount) break;
+        
         auto roadPos = ctx.get<Position>(road);
         auto roadRot = ctx.get<Rotation>(road);
 
@@ -285,9 +401,12 @@ inline void collectMapObservationsSystem(Engine &ctx,
         }
 
         map_obs.obs[arrIndex] = referenceFrame.observationOf(
-            roadPos, roadRot, ctx.get<Scale>(road), ctx.get<EntityType>(road), static_cast<float>(ctx.get<RoadMapId>(road).id), ctx.get<MapType>(road));
+            roadPos, roadRot, ctx.get<Scale>(road), ctx.get<EntityType>(road), 
+            static_cast<float>(ctx.get<RoadMapId>(road).id), ctx.get<MapType>(road));
         arrIndex++;
     }
+    
+    // Zero out remaining entries
     while (arrIndex < consts::kMaxAgentMapObservationsCount) {
         map_obs.obs[arrIndex++] = MapObservation::zero();
     }

From 442b21062f9735178684e228c30412e6952fab04 Mon Sep 17 00:00:00 2001
From: Robin Sieber <rjs02@hotmail.com>
Date: Wed, 4 Jun 2025 11:54:57 +0200
Subject: [PATCH 26/38] add occlusion check with nested loop instead of bvh

---
 src/sim.cpp | 86 +++++++++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 84 insertions(+), 2 deletions(-)

diff --git a/src/sim.cpp b/src/sim.cpp
index 2ce493985..d11417fdc 100755
--- a/src/sim.cpp
+++ b/src/sim.cpp
@@ -289,7 +289,88 @@ inline void filterByOcclusion(Engine &ctx,
             
             // If we hit something before reaching the target, it's occluded
             return (hit_entity != Entity::none() && hit_entity != target);
-        }), objects.end());
+        }), objects.end()); // erase-remove idiom
+}
+
+inline void filterByOcclusionAll(Engine &ctx,
+                                 const ViewField &vf, // Contains observer's position
+                                 std::vector<Entity> &objects_to_filter // List of potential targets
+                                 )
+{
+    using namespace madrona::math;
+
+    Vector3 observer_pos_3d = {vf.position.x, vf.position.y, vf.position.z};
+
+    std::vector<Entity> visible_entities;
+    visible_entities.reserve(objects_to_filter.size());
+
+
+    for (Entity target_entity : objects_to_filter) {
+        const Position& target_pos_comp = ctx.get<Position>(target_entity);
+        // Assuming Position component includes Z, or it's implicitly handled (e.g., all agents at a fixed Z for this check if needed)
+        Vector3 target_pos_3d = {target_pos_comp.x, target_pos_comp.y, target_pos_comp.z}; 
+
+        Vector3 vec_observer_to_target = target_pos_3d - observer_pos_3d;
+        float dist_to_target = vec_observer_to_target.length();
+        
+        if (dist_to_target < 0.1f) { // add to visible if really close
+            visible_entities.push_back(target_entity);
+            continue;
+        }
+        Vector3 normalized_ray_dir_to_target = vec_observer_to_target / dist_to_target;
+
+        bool is_target_occluded = false;
+
+        for (Entity potential_occluder_entity : objects_to_filter) {
+            if (potential_occluder_entity == target_entity) {
+                continue; // Don't check occlusion against self
+            }
+
+            const Position& occluder_pos_comp = ctx.get<Position>(potential_occluder_entity);
+            const Rotation& occluder_rot_comp = ctx.get<Rotation>(potential_occluder_entity);
+            const VehicleSize& occluder_vehicle_size = ctx.get<VehicleSize>(potential_occluder_entity);
+
+            Diag3x3 occluder_dims_from_vehicle_size = {
+                occluder_vehicle_size.width,
+                occluder_vehicle_size.length,
+                occluder_vehicle_size.height 
+            };
+
+
+            // unit aabb
+            AABB local_aabb = {
+                {-0.5f, -0.5f, -0.5f},
+                { 0.5f,  0.5f,  0.5f}
+            };
+            
+            // transform unit aabb to world space
+            AABB world_occluder_aabb = local_aabb.applyTRS(
+                occluder_pos_comp, 
+                occluder_rot_comp,
+                occluder_dims_from_vehicle_size
+            );
+
+            // inverse ray direction
+            Diag3x3 inv_ray_d = {
+                (normalized_ray_dir_to_target.x == 0.f) ? copysignf(FLT_MAX, normalized_ray_dir_to_target.x) : 1.f / normalized_ray_dir_to_target.x,
+                (normalized_ray_dir_to_target.y == 0.f) ? copysignf(FLT_MAX, normalized_ray_dir_to_target.y) : 1.f / normalized_ray_dir_to_target.y,
+                (normalized_ray_dir_to_target.z == 0.f) ? copysignf(FLT_MAX, normalized_ray_dir_to_target.z) : 1.f / normalized_ray_dir_to_target.z
+            };
+
+            float hit_t_occluder = 0.f; 
+            // Check intersection from a small epsilon away from observer up to just before the target
+            if (world_occluder_aabb.rayIntersects(observer_pos_3d, inv_ray_d, 0.01f, dist_to_target - 0.01f, hit_t_occluder)) {
+                is_target_occluded = true;
+                break; 
+            }
+        }
+
+        if (!is_target_occluded) {
+            visible_entities.push_back(target_entity);
+        }
+    }
+
+    objects_to_filter = visible_entities;
 }
 
 inline void collectPartnerObsSystem(Engine &ctx,
@@ -319,7 +400,8 @@ inline void collectPartnerObsSystem(Engine &ctx,
         
     // narrow phase: raycast to get occlusion by other dynamic objects
     if (ctx.data().params.viewOccludeObjects) {
-        filterByOcclusion(ctx, vf, objectsDynamic);
+        //filterByOcclusion(ctx, vf, objectsDynamic);
+        filterByOcclusionAll(ctx, vf, objectsDynamic);
     }
 
     // add observations to array partner_obs.obs based on filtered list

From e7ebc1058f918906597988b128013351631f70e7 Mon Sep 17 00:00:00 2001
From: Robin Sieber <rjs02@hotmail.com>
Date: Wed, 4 Jun 2025 15:34:30 +0200
Subject: [PATCH 27/38] ray trace against 8 corner points instead of only
 center

---
 src/sim.cpp | 117 ++++++++++++++++++++++++++++++----------------------
 1 file changed, 67 insertions(+), 50 deletions(-)

diff --git a/src/sim.cpp b/src/sim.cpp
index d11417fdc..1d39f8e28 100755
--- a/src/sim.cpp
+++ b/src/sim.cpp
@@ -305,71 +305,88 @@ inline void filterByOcclusionAll(Engine &ctx,
     visible_entities.reserve(objects_to_filter.size());
 
 
-    for (Entity target_entity : objects_to_filter) {
+    for (Entity& target_entity : objects_to_filter) {
         const Position& target_pos_comp = ctx.get<Position>(target_entity);
-        // Assuming Position component includes Z, or it's implicitly handled (e.g., all agents at a fixed Z for this check if needed)
-        Vector3 target_pos_3d = {target_pos_comp.x, target_pos_comp.y, target_pos_comp.z}; 
+        const Rotation& target_rot_comp = ctx.get<Rotation>(target_entity);
+        const VehicleSize& target_vehicle_size = ctx.get<VehicleSize>(target_entity);
 
-        Vector3 vec_observer_to_target = target_pos_3d - observer_pos_3d;
-        float dist_to_target = vec_observer_to_target.length();
-        
-        if (dist_to_target < 0.1f) { // add to visible if really close
-            visible_entities.push_back(target_entity);
-            continue;
-        }
-        Vector3 normalized_ray_dir_to_target = vec_observer_to_target / dist_to_target;
+        Vector3 target_world_center = {target_pos_comp.x, target_pos_comp.y, target_pos_comp.z};
 
-        bool is_target_occluded = false;
+        float half_width = target_vehicle_size.width / 2.f;
+        float half_length = target_vehicle_size.length / 2.f;
+        float half_height = target_vehicle_size.height / 2.f;
 
-        for (Entity potential_occluder_entity : objects_to_filter) {
-            if (potential_occluder_entity == target_entity) {
-                continue; // Don't check occlusion against self
-            }
+        Vector3 local_corners[8] = {
+            {-half_width, -half_length, -half_height}, { half_width, -half_length, -half_height},
+            {-half_width,  half_length, -half_height}, { half_width,  half_length, -half_height},
+            {-half_width, -half_length,  half_height}, { half_width, -half_length,  half_height},
+            {-half_width,  half_length,  half_height}, { half_width,  half_length,  half_height}
+        };
 
-            const Position& occluder_pos_comp = ctx.get<Position>(potential_occluder_entity);
-            const Rotation& occluder_rot_comp = ctx.get<Rotation>(potential_occluder_entity);
-            const VehicleSize& occluder_vehicle_size = ctx.get<VehicleSize>(potential_occluder_entity);
+        int unoccluded_corner_rays = 0;
 
-            Diag3x3 occluder_dims_from_vehicle_size = {
-                occluder_vehicle_size.width,
-                occluder_vehicle_size.length,
-                occluder_vehicle_size.height 
-            };
+        for (int i = 0; i < 8; ++i) { // check all corners
+            Vector3 target_corner_world = target_world_center + target_rot_comp.rotateVec(local_corners[i]);
+            
+            Vector3 vec_observer_to_target_corner = target_corner_world - observer_pos_3d;
+            float dist_to_target_corner = vec_observer_to_target_corner.length();
 
+            if (dist_to_target_corner < 0.1f) { // add to visible if really close
+                unoccluded_corner_rays++;
+                break; // early return since one clear ray is enough
+            }
+            Vector3 normalized_ray_dir_to_target_corner = vec_observer_to_target_corner / dist_to_target_corner;
 
-            // unit aabb
-            AABB local_aabb = {
-                {-0.5f, -0.5f, -0.5f},
-                { 0.5f,  0.5f,  0.5f}
-            };
-            
-            // transform unit aabb to world space
-            AABB world_occluder_aabb = local_aabb.applyTRS(
-                occluder_pos_comp, 
-                occluder_rot_comp,
-                occluder_dims_from_vehicle_size
-            );
-
-            // inverse ray direction
-            Diag3x3 inv_ray_d = {
-                (normalized_ray_dir_to_target.x == 0.f) ? copysignf(FLT_MAX, normalized_ray_dir_to_target.x) : 1.f / normalized_ray_dir_to_target.x,
-                (normalized_ray_dir_to_target.y == 0.f) ? copysignf(FLT_MAX, normalized_ray_dir_to_target.y) : 1.f / normalized_ray_dir_to_target.y,
-                (normalized_ray_dir_to_target.z == 0.f) ? copysignf(FLT_MAX, normalized_ray_dir_to_target.z) : 1.f / normalized_ray_dir_to_target.z
-            };
+            bool corner_ray_occluded = false;
+            for (Entity& potential_occluder_entity : objects_to_filter) {
+                if (potential_occluder_entity == target_entity) {
+                    continue; // Don't check occlusion against self
+                }
 
-            float hit_t_occluder = 0.f; 
-            // Check intersection from a small epsilon away from observer up to just before the target
-            if (world_occluder_aabb.rayIntersects(observer_pos_3d, inv_ray_d, 0.01f, dist_to_target - 0.01f, hit_t_occluder)) {
-                is_target_occluded = true;
-                break; 
+                const Position& occluder_pos_comp = ctx.get<Position>(potential_occluder_entity);
+                const Rotation& occluder_rot_comp = ctx.get<Rotation>(potential_occluder_entity);
+                const VehicleSize& occluder_vehicle_size = ctx.get<VehicleSize>(potential_occluder_entity);
+
+                Diag3x3 occluder_dims_from_vehicle_size = {
+                    occluder_vehicle_size.width, 
+                    occluder_vehicle_size.length,
+                    occluder_vehicle_size.height 
+                };
+
+                AABB local_aabb = {
+                    {-0.5f, -0.5f, -0.5f},
+                    { 0.5f,  0.5f,  0.5f}
+                };
+                
+                AABB world_occluder_aabb = local_aabb.applyTRS(
+                    occluder_pos_comp, 
+                    occluder_rot_comp,
+                    occluder_dims_from_vehicle_size
+                );
+
+                // inverse ray direction
+                Diag3x3 inv_ray_d = {
+                    (normalized_ray_dir_to_target_corner.x == 0.f) ? copysignf(FLT_MAX, normalized_ray_dir_to_target_corner.x) : 1.f / normalized_ray_dir_to_target_corner.x,
+                    (normalized_ray_dir_to_target_corner.y == 0.f) ? copysignf(FLT_MAX, normalized_ray_dir_to_target_corner.y) : 1.f / normalized_ray_dir_to_target_corner.y,
+                    (normalized_ray_dir_to_target_corner.z == 0.f) ? copysignf(FLT_MAX, normalized_ray_dir_to_target_corner.z) : 1.f / normalized_ray_dir_to_target_corner.z
+                };
+
+                float hit_t_occluder = 0.f; 
+                if (world_occluder_aabb.rayIntersects(observer_pos_3d, inv_ray_d, 0.01f, dist_to_target_corner - 0.01f, hit_t_occluder)) {
+                    corner_ray_occluded = true;
+                    break; 
+                }
+            }
+            if (!corner_ray_occluded) {
+                unoccluded_corner_rays++;
             }
         }
 
-        if (!is_target_occluded) {
+        if (unoccluded_corner_rays > 0) {
             visible_entities.push_back(target_entity);
         }
     }
-
+    
     objects_to_filter = visible_entities;
 }
 

From 6f28c638d2f241205a58c2538cbaaa268f116a5d Mon Sep 17 00:00:00 2001
From: Robin Sieber <rjs02@hotmail.com>
Date: Wed, 4 Jun 2025 22:51:42 +0200
Subject: [PATCH 28/38] change vector to array for gpu compatibility

---
 src/sim.cpp | 115 ++++++++++++++++++++++++++++++----------------------
 1 file changed, 66 insertions(+), 49 deletions(-)

diff --git a/src/sim.cpp b/src/sim.cpp
index 1d39f8e28..6a494e7e3 100755
--- a/src/sim.cpp
+++ b/src/sim.cpp
@@ -207,14 +207,15 @@ struct ViewField {
 };
 
 // Broad-phase filtering: Get dynamic objects that are potentially visible within the view field
-inline std::vector<Entity> visibleCandidatesDynamic(Engine &ctx, 
-                                                    const ViewField &vf,
-                                                    const OtherAgents &other_agents,
-                                                    Entity self_entity) {
-    std::vector<Entity> candidates;
+inline CountT visibleCandidatesDynamic(Engine &ctx, 
+                                       const ViewField &vf,
+                                       const OtherAgents &other_agents,
+                                       Entity self_entity,
+                                       Entity* candidates_buffer) {
+    CountT count = 0;
     
     // Simple radius-based broad phase filtering for now
-    for (CountT agentIdx = 0; agentIdx < ctx.data().numAgents - 1; ++agentIdx) {
+    for (CountT agentIdx = 0; agentIdx < ctx.data().numAgents - 1 && count < consts::kMaxAgentCount - 1; ++agentIdx) {
         Entity other = other_agents.e[agentIdx];
         if (other == self_entity) continue; // Don't include self
         
@@ -230,21 +231,22 @@ inline std::vector<Entity> visibleCandidatesDynamic(Engine &ctx,
             float angle = acosf(fmaxf(-1.0f, fminf(1.0f, dot_product))); // clamp to [-1, 1]
             
             if (angle <= vf.viewAngle * 0.5f) {
-                candidates.push_back(other);
+                candidates_buffer[count++] = other;
             }
         }
     }
     
-    return candidates;
+    return count;
 }
 
 // Broad-phase filtering: Get static road objects that are potentially visible within the view field
-inline std::vector<Entity> visibleCandidatesStatic(Engine &ctx,
-                                                   const ViewField &vf) {
-    std::vector<Entity> candidates;
+inline CountT visibleCandidatesStatic(Engine &ctx,
+                                      const ViewField &vf,
+                                      Entity* candidates_buffer) {
+    CountT count = 0;
     
     // Simple radius-based broad phase filtering for roads
-    for (CountT roadIdx = 0; roadIdx < ctx.data().numRoads; ++roadIdx) {
+    for (CountT roadIdx = 0; roadIdx < ctx.data().numRoads && count < consts::kMaxAgentMapObservationsCount; ++roadIdx) {
         Entity road = ctx.data().roads[roadIdx];
         const Position &road_pos = ctx.get<Position>(road);
         Vector2 relative_pos = (road_pos - vf.position).xy();
@@ -258,54 +260,59 @@ inline std::vector<Entity> visibleCandidatesStatic(Engine &ctx,
             float angle = acosf(fmaxf(-1.0f, fminf(1.0f, dot_product))); // clamp to [-1, 1]
             
             if (angle <= vf.viewAngle * 0.5f) {
-                candidates.push_back(road);
+                candidates_buffer[count++] = road;
             }
         }
     }
     
-    return candidates;
+    return count;
 }
 
 // Narrow-phase filtering: Check for occlusion using simple ray casting
 inline void filterByOcclusion(Engine &ctx,
                               const ViewField &vf,
-                              std::vector<Entity> &objects) {
+                              Span<Entity> objects) {
     auto &bvh = ctx.singleton<broadphase::BVH>();
     Vector3 observer_pos = {vf.position.x, vf.position.y, vf.position.z};
     
-    // Filter out occluded objects
-    objects.erase(std::remove_if(objects.begin(), objects.end(), 
-        [&](Entity target) {
-            const Position &target_pos = ctx.get<Position>(target);
-            Vector3 target_pos_3d = {target_pos.x, target_pos.y, target_pos.z};
-            
-            Vector3 ray_dir = (target_pos_3d - observer_pos).normalize();
-            float target_distance = (target_pos_3d - observer_pos).length();
-            
-            // Trace ray from observer to target
-            float hit_t;
-            Vector3 hit_normal;
-            Entity hit_entity = bvh.traceRay(observer_pos, ray_dir, &hit_t, &hit_normal, target_distance - 0.1f);
-            
-            // If we hit something before reaching the target, it's occluded
-            return (hit_entity != Entity::none() && hit_entity != target);
-        }), objects.end()); // erase-remove idiom
+    // Filter out occluded objects in-place
+    CountT write_idx = 0;
+    for (CountT read_idx = 0; read_idx < objects.size(); ++read_idx) {
+        Entity target = objects[read_idx];
+        const Position &target_pos = ctx.get<Position>(target);
+        Vector3 target_pos_3d = {target_pos.x, target_pos.y, target_pos.z};
+        
+        Vector3 ray_dir = (target_pos_3d - observer_pos).normalize();
+        float target_distance = (target_pos_3d - observer_pos).length();
+        
+        // Trace ray from observer to target
+        float hit_t;
+        Vector3 hit_normal;
+        Entity hit_entity = bvh.traceRay(observer_pos, ray_dir, &hit_t, &hit_normal, target_distance - 0.1f);
+        
+        // If we didn't hit something before reaching the target, it's not occluded
+        if (hit_entity == Entity::none() || hit_entity == target) {
+            objects[write_idx++] = target;
+        }
+    }
+    
+    // Update the effective size (though we can't change span size, caller needs to track this)
+    // This is handled by returning the new count
 }
 
-inline void filterByOcclusionAll(Engine &ctx,
-                                 const ViewField &vf, // Contains observer's position
-                                 std::vector<Entity> &objects_to_filter // List of potential targets
-                                 )
+inline CountT filterByOcclusionAll(Engine &ctx,
+                                   const ViewField &vf, // Contains observer's position
+                                   Span<Entity> objects_to_filter // List of potential targets
+                                   )
 {
     using namespace madrona::math;
 
     Vector3 observer_pos_3d = {vf.position.x, vf.position.y, vf.position.z};
 
-    std::vector<Entity> visible_entities;
-    visible_entities.reserve(objects_to_filter.size());
-
+    CountT visible_count = 0;
 
-    for (Entity& target_entity : objects_to_filter) {
+    for (CountT target_idx = 0; target_idx < objects_to_filter.size(); ++target_idx) {
+        Entity target_entity = objects_to_filter[target_idx];
         const Position& target_pos_comp = ctx.get<Position>(target_entity);
         const Rotation& target_rot_comp = ctx.get<Rotation>(target_entity);
         const VehicleSize& target_vehicle_size = ctx.get<VehicleSize>(target_entity);
@@ -338,7 +345,8 @@ inline void filterByOcclusionAll(Engine &ctx,
             Vector3 normalized_ray_dir_to_target_corner = vec_observer_to_target_corner / dist_to_target_corner;
 
             bool corner_ray_occluded = false;
-            for (Entity& potential_occluder_entity : objects_to_filter) {
+            for (CountT occluder_idx = 0; occluder_idx < objects_to_filter.size(); ++occluder_idx) {
+                Entity potential_occluder_entity = objects_to_filter[occluder_idx];
                 if (potential_occluder_entity == target_entity) {
                     continue; // Don't check occlusion against self
                 }
@@ -383,11 +391,11 @@ inline void filterByOcclusionAll(Engine &ctx,
         }
 
         if (unoccluded_corner_rays > 0) {
-            visible_entities.push_back(target_entity);
+            objects_to_filter[visible_count++] = target_entity;
         }
     }
     
-    objects_to_filter = visible_entities;
+    return visible_count;
 }
 
 inline void collectPartnerObsSystem(Engine &ctx,
@@ -413,17 +421,19 @@ inline void collectPartnerObsSystem(Engine &ctx,
     ViewField vf(pos, heading, viewRadius, viewAngle);
 
     // broad phase: remove if not in view field + remove self
-    auto objectsDynamic = visibleCandidatesDynamic(ctx, vf, other_agents, agent_iface.e);
+    Entity objectsDynamicBuffer[consts::kMaxAgentCount - 1];
+    CountT dynamicCount = visibleCandidatesDynamic(ctx, vf, other_agents, agent_iface.e, objectsDynamicBuffer);
+    Span<Entity> objectsDynamic(objectsDynamicBuffer, dynamicCount);
         
     // narrow phase: raycast to get occlusion by other dynamic objects
     if (ctx.data().params.viewOccludeObjects) {
-        //filterByOcclusion(ctx, vf, objectsDynamic);
-        filterByOcclusionAll(ctx, vf, objectsDynamic);
+        dynamicCount = filterByOcclusionAll(ctx, vf, objectsDynamic);
     }
 
     // add observations to array partner_obs.obs based on filtered list
     CountT arrIndex = 0;
-    for (Entity other : objectsDynamic) {
+    for (CountT i = 0; i < dynamicCount; ++i) {
+        Entity other = objectsDynamicBuffer[i];
         // future optimization: k nearest maybe in case of many agents in view
         if (arrIndex >= consts::kMaxAgentCount - 1) break;
         
@@ -474,7 +484,13 @@ inline void collectMapObservationsSystem(Engine &ctx,
     float view_angle = ctx.data().params.viewConeHalfAngle * 2.0f;
     
     ViewField vf(pos, heading, view_radius, view_angle);
-    auto objectsStatic = visibleCandidatesStatic(ctx, vf);
+    
+    // broad phase: remove if not in view field + remove self
+    Entity objectsStaticBuffer[consts::kMaxAgentMapObservationsCount];
+    CountT staticCount = visibleCandidatesStatic(ctx, vf, objectsStaticBuffer);
+
+    // Further filtering: narrow phase based on occlusion from dynamic objects
+    // But this would require removing the parallelism of the collectPartnerObsSystem and collectMapObservationsSystem
 
     const auto alg = ctx.data().params.roadObservationAlgorithm;
     if (alg == FindRoadObservationsWith::KNearestEntitiesWithRadiusFiltering) {
@@ -488,7 +504,8 @@ inline void collectMapObservationsSystem(Engine &ctx,
     utils::ReferenceFrame referenceFrame(pos.xy(), rot);
     CountT arrIndex = 0;
     
-    for (Entity road : objectsStatic) {
+    for (CountT i = 0; i < staticCount; ++i) {
+        Entity road = objectsStaticBuffer[i];
         if (arrIndex >= consts::kMaxAgentMapObservationsCount) break;
         
         auto roadPos = ctx.get<Position>(road);

From 254deb43eb07c81e51a3d1755a0963a947561734 Mon Sep 17 00:00:00 2001
From: Robin Sieber <rjs02@hotmail.com>
Date: Thu, 5 Jun 2025 13:41:32 +0200
Subject: [PATCH 29/38] resolved rebase conflicts with dev

---
 gpudrive/env/config.py     |  6 ++--
 gpudrive/env/env_torch.py  | 11 +++++--
 gpudrive/visualize/core.py | 62 ++++++++++++++++++++++++--------------
 src/sim.cpp                | 18 ++++++-----
 4 files changed, 64 insertions(+), 33 deletions(-)

diff --git a/gpudrive/env/config.py b/gpudrive/env/config.py
index d0f1d8bee..cb525a164 100755
--- a/gpudrive/env/config.py
+++ b/gpudrive/env/config.py
@@ -57,7 +57,7 @@ class EnvConfig:
     # Road observation algorithm settings
     road_obs_algorithm: str = "linear"  # Algorithm for road observations
     obs_radius: float = 50.0  # Radius for road observations
-    view_cone_half_angle: float = torch.pi / 1.5  # Half-angle for view cone setting
+    view_cone_half_angle: float = torch.pi / 3  # Half-angle for view cone setting
     view_occlude_objects: bool = False  # True: Vehicles are removed from observations if they are occluded by other vehicles
     polyline_reduction_threshold: float = (
         0.1  # Threshold for polyline reduction
@@ -84,11 +84,13 @@ class EnvConfig:
     pedestrian_accel_range: Tuple[float, float] = (-1.5, 1.5)  # m/s²
     pedestrian_steer_range: Tuple[float, float] = (-3.14, 3.14)  # radians (±180°)
 
-    head_tilt_actions: torch.Tensor = torch.Tensor([0])
+    # Head tilt action range
+    head_tilt_action_range: Tuple[float, float] = (-0.7854, 0.7854)  # radians (±45°)
 
     # Classic or Invertible Bicycle dynamics model
     action_space_steer_disc: int = 13
     action_space_accel_disc: int = 7
+    action_space_head_tilt_disc: int = 7
 
     # Delta Local dynamics model
     dx: torch.Tensor = torch.round(torch.linspace(-2.0, 2.0, 20), decimals=3)
diff --git a/gpudrive/env/env_torch.py b/gpudrive/env/env_torch.py
index d184fa50a..edc5925f3 100755
--- a/gpudrive/env/env_torch.py
+++ b/gpudrive/env/env_torch.py
@@ -1165,8 +1165,14 @@ def _set_discrete_action_space(self) -> None:
                 ),
                 decimals=3,
             ).to(self.device)
-            self.head_actions = self.config.head_tilt_actions.to(self.device)
-
+            self.head_actions = torch.round(
+                torch.linspace(
+                    self.config.head_tilt_action_range[0],
+                    self.config.head_tilt_action_range[1],
+                    self.config.action_space_head_tilt_disc,
+                ),
+                decimals=3,
+            ).to(self.device)
             products = product(
                 self.accel_actions, self.steer_actions, self.head_actions
             )
@@ -2045,6 +2051,7 @@ def render(self, focus_env_idx=0, focus_agent_idx=[0, 1]):
                     focus_env_idx, agent_idx
                 ].item(),
                 route_progress=self.route_progress[agent_idx],
+                previous_actions=self.previous_action_value_tensor # for head angle
             )
             agent_views.append(agent_obs)
 
diff --git a/gpudrive/visualize/core.py b/gpudrive/visualize/core.py
index 4dab953e2..a231a2fab 100644
--- a/gpudrive/visualize/core.py
+++ b/gpudrive/visualize/core.py
@@ -5,7 +5,7 @@
 from typing import Tuple, Optional, List, Dict, Any, Union
 import seaborn as sns
 import matplotlib.pyplot as plt
-from matplotlib.patches import Circle
+from matplotlib.patches import Circle, Arc
 from matplotlib.lines import Line2D
 from matplotlib.collections import LineCollection
 from mpl_toolkits.mplot3d import Axes3D
@@ -1702,6 +1702,8 @@ def plot_agent_observation(
         trajectory: Optional[np.ndarray] = None,
         step_reward: Optional[float] = None,
         route_progress: Optional[float] = None,
+        head_angle: Optional[float] = 0.0,
+        previous_actions: Optional[torch.Tensor] = None
     ):
         """
         Plot observation from agent POV to inspect the information available
@@ -2013,28 +2015,44 @@ def plot_agent_observation(
         ax.set_xlim((-self.env_config.obs_radius, self.env_config.obs_radius))
         ax.set_ylim((-self.env_config.obs_radius, self.env_config.obs_radius))
 
-        # Add a circle representing the observation radius
-        observation_circle = Circle(
-            (0, 0),  # Center at origin
-            radius=self.env_config.obs_radius,
-            color="black",
-            fill=False,
-            linestyle="-",
-            linewidth=1.0,
-            alpha=0.7,
-        )
-
-        ax.add_patch(observation_circle)
-
         # Add V lines for view cone boundaries
-        # x1 = self.env_config.obs_radius * np.cos(self.env_config.view_cone_half_angle)
-        # y1 = self.env_config.obs_radius * np.sin(self.env_config.view_cone_half_angle)
-
-        # x2 = self.env_config.obs_radius * np.cos(-self.env_config.view_cone_half_angle)
-        # y2 = self.env_config.obs_radius * np.sin(-self.env_config.view_cone_half_angle)
-
-        # ax.add_line(Line2D([0, x1], [0, y1], color="#FF9900", linewidth=1.5, linestyle="--"))
-        # ax.add_line(Line2D([0, x2], [0, y2], color="#FF9900", linewidth=1.5, linestyle="--"))
+        # must be classic or bicycle to have head angle
+        if previous_actions is not None and \
+           hasattr(self.env_config, 'dynamics_model') and \
+           self.env_config.dynamics_model in ["classic", "bicycle"] and \
+           previous_actions.ndim == 3 and \
+           env_idx < previous_actions.shape[0] and \
+           agent_idx < previous_actions.shape[1] and \
+           previous_actions.shape[2] == 3: # Expected shape [worlds, agents, 3 (accel, steer, head)]
+            headAngle = previous_actions[env_idx, agent_idx, 2].item()
+        else:
+            headAngle = head_angle
+
+        # Calculate angles for the arc and view cone lines, incorporating head_angle_to_use
+        angle1_rad = headAngle + self.env_config.view_cone_half_angle
+        angle2_rad = headAngle - self.env_config.view_cone_half_angle
+
+        x1_rot = self.env_config.obs_radius * np.cos(angle1_rad)
+        y1_rot = self.env_config.obs_radius * np.sin(angle1_rad)
+
+        x2_rot = self.env_config.obs_radius * np.cos(angle2_rad)
+        y2_rot = self.env_config.obs_radius * np.sin(angle2_rad)
+
+        ax.add_line(Line2D([0, x1_rot], [0, y1_rot], color="#FF9900", linewidth=1.5, linestyle="--"))
+        ax.add_line(Line2D([0, x2_rot], [0, y2_rot], color="#FF9900", linewidth=1.5, linestyle="--"))
+
+        # Plot observation radius as an Arc
+        obs_arc = Arc((0,0),
+                      width=2*self.env_config.obs_radius,
+                      height=2*self.env_config.obs_radius,
+                      angle=0, # Default orientation
+                      theta1=np.degrees(angle2_rad),
+                      theta2=np.degrees(angle1_rad),
+                      color="black",
+                      linestyle="-",
+                      linewidth=1.0,
+                      alpha=0.7)
+        ax.add_patch(obs_arc)
 
         ax.set_xticks([])
         ax.set_yticks([])
diff --git a/src/sim.cpp b/src/sim.cpp
index 6a494e7e3..48d773ca4 100755
--- a/src/sim.cpp
+++ b/src/sim.cpp
@@ -201,9 +201,10 @@ struct ViewField {
     const float heading;
     const float viewRadius;
     const float viewAngle;
+    const float headAngle;
 
-    ViewField(const Position& position, const float heading, const float viewRadius, const float viewAngle)
-        : position(position), heading(heading), viewRadius(viewRadius), viewAngle(viewAngle) {}
+    ViewField(const Position& position, const float heading, const float viewRadius, const float viewAngle, const float headAngle)
+        : position(position), heading(heading), viewRadius(viewRadius), viewAngle(viewAngle), headAngle(headAngle) {}
 };
 
 // Broad-phase filtering: Get dynamic objects that are potentially visible within the view field
@@ -225,7 +226,7 @@ inline CountT visibleCandidatesDynamic(Engine &ctx,
         // Check if within view radius
         if (relative_pos.length() <= vf.viewRadius) {
             // Check if within view cone
-            Vector2 forward_dir = {cosf(vf.heading), sinf(vf.heading)};
+            Vector2 forward_dir = {cosf(vf.heading + vf.headAngle), sinf(vf.heading + vf.headAngle)};
             Vector2 normalized_relative_pos = relative_pos / relative_pos.length();
             float dot_product = normalized_relative_pos.dot(forward_dir);
             float angle = acosf(fmaxf(-1.0f, fminf(1.0f, dot_product))); // clamp to [-1, 1]
@@ -254,7 +255,7 @@ inline CountT visibleCandidatesStatic(Engine &ctx,
         // Check if within view radius
         if (relative_pos.length() <= vf.viewRadius) {
             // Check if within view cone
-            Vector2 forward_dir = {cosf(vf.heading), sinf(vf.heading)};
+            Vector2 forward_dir = {cosf(vf.heading + vf.headAngle), sinf(vf.heading + vf.headAngle)};
             Vector2 normalized_relative_pos = relative_pos / relative_pos.length();
             float dot_product = normalized_relative_pos.dot(forward_dir);
             float angle = acosf(fmaxf(-1.0f, fminf(1.0f, dot_product))); // clamp to [-1, 1]
@@ -412,13 +413,14 @@ inline void collectPartnerObsSystem(Engine &ctx,
 
     // to write partner features into array
     auto &partner_obs = ctx.get<PartnerObservations>(agent_iface.e);
-
     // Get agent's heading from rotation
     float heading = utils::quatToYaw(rot);
     float viewRadius = ctx.data().params.observationRadius;
     float viewAngle = ctx.data().params.viewConeHalfAngle * 2.0f; // Convert half-angle to full angle
+    const Action &action = ctx.get<Action>(agent_iface.e);
+    float headAngle = ctx.get<ControlledState>(agent_iface.e).controlled ? action.classic.headAngle : 0.f;
     
-    ViewField vf(pos, heading, viewRadius, viewAngle);
+    ViewField vf(pos, heading, viewRadius, viewAngle, headAngle);
 
     // broad phase: remove if not in view field + remove self
     Entity objectsDynamicBuffer[consts::kMaxAgentCount - 1];
@@ -482,8 +484,10 @@ inline void collectMapObservationsSystem(Engine &ctx,
     float heading = utils::quatToYaw(rot);
     float view_radius = ctx.data().params.observationRadius;
     float view_angle = ctx.data().params.viewConeHalfAngle * 2.0f;
+    const Action &action = ctx.get<Action>(agent_iface.e);
+    float headAngle = ctx.get<ControlledState>(agent_iface.e).controlled ? action.classic.headAngle : 0.f;
     
-    ViewField vf(pos, heading, view_radius, view_angle);
+    ViewField vf(pos, heading, view_radius, view_angle, headAngle);
     
     // broad phase: remove if not in view field + remove self
     Entity objectsStaticBuffer[consts::kMaxAgentMapObservationsCount];

From 1138290e5560bef90c3a5360691181f688ef29cf Mon Sep 17 00:00:00 2001
From: Robin Sieber <rjs02@hotmail.com>
Date: Thu, 5 Jun 2025 21:48:42 +0200
Subject: [PATCH 30/38] add action for head tilt animation

---
 gpudrive/env/env_puffer.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/gpudrive/env/env_puffer.py b/gpudrive/env/env_puffer.py
index ee4bae1d3..341fa22dd 100644
--- a/gpudrive/env/env_puffer.py
+++ b/gpudrive/env/env_puffer.py
@@ -259,7 +259,7 @@ def __init__(
         self.total_agents = self.max_cont_agents_per_env * self.num_worlds
 
         # Set working directory to the base directory 'gpudrive'
-        working_dir = os.path.join(Path.cwd(), "../gpudrive")
+        working_dir = os.path.join(Path.cwd(), "../gpudrive-emerge")
         os.chdir(working_dir)
 
         # Make env
@@ -665,6 +665,7 @@ def render_env(self):
                     ),
                     step_reward=self.env.guidance_reward[0, agent_idx].item(),
                     route_progress=self.env.route_progress[agent_idx].item(),
+                    previous_actions=self.env.previous_action_value_tensor,
                 )
                 self.agent_frames[agent_idx].append(img_from_fig(agent_obs))
 

From 909655fdfa0eb3bc096bfab31eb68692a64ac81e Mon Sep 17 00:00:00 2001
From: Robin Sieber <rjs02@hotmail.com>
Date: Fri, 6 Jun 2025 21:50:30 +0200
Subject: [PATCH 31/38] change line style

---
 gpudrive/visualize/core.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/gpudrive/visualize/core.py b/gpudrive/visualize/core.py
index a231a2fab..de5ec020f 100644
--- a/gpudrive/visualize/core.py
+++ b/gpudrive/visualize/core.py
@@ -2038,8 +2038,8 @@ def plot_agent_observation(
         x2_rot = self.env_config.obs_radius * np.cos(angle2_rad)
         y2_rot = self.env_config.obs_radius * np.sin(angle2_rad)
 
-        ax.add_line(Line2D([0, x1_rot], [0, y1_rot], color="#FF9900", linewidth=1.5, linestyle="--"))
-        ax.add_line(Line2D([0, x2_rot], [0, y2_rot], color="#FF9900", linewidth=1.5, linestyle="--"))
+        ax.add_line(Line2D([0, x1_rot], [0, y1_rot], color="k", linewidth=1.0, linestyle="-"))
+        ax.add_line(Line2D([0, x2_rot], [0, y2_rot], color="k", linewidth=1.0, linestyle="-"))
 
         # Plot observation radius as an Arc
         obs_arc = Arc((0,0),
@@ -2048,9 +2048,9 @@ def plot_agent_observation(
                       angle=0, # Default orientation
                       theta1=np.degrees(angle2_rad),
                       theta2=np.degrees(angle1_rad),
-                      color="black",
-                      linestyle="-",
-                      linewidth=1.0,
+                      color="k", 
+                      linestyle="-", 
+                      linewidth=1.0, 
                       alpha=0.7)
         ax.add_patch(obs_arc)
 

From cd3ccf3478c8bce21742e00b741d44e0ab2af0e8 Mon Sep 17 00:00:00 2001
From: Robin Sieber <rjs02@hotmail.com>
Date: Sun, 8 Jun 2025 13:21:19 +0200
Subject: [PATCH 32/38] update config files to new parameters

---
 baselines/ppo/config/ppo_guided_autonomy.yaml |  8 ++-
 baselines/ppo/ppo_guided_autonomy.py          | 19 +++++++
 gpudrive/env/env_puffer.py                    |  2 +-
 gpudrive/networks/agents.py                   | 49 +++++++++++++++++++
 4 files changed, 76 insertions(+), 2 deletions(-)

diff --git a/baselines/ppo/config/ppo_guided_autonomy.yaml b/baselines/ppo/config/ppo_guided_autonomy.yaml
index c87ea916c..1e11cd4e7 100644
--- a/baselines/ppo/config/ppo_guided_autonomy.yaml
+++ b/baselines/ppo/config/ppo_guided_autonomy.yaml
@@ -42,15 +42,21 @@ environment: # Overrides default environment configs (see pygpudrive/env/config.
   polyline_reduction_threshold: 0.1 # Rate at which to sample points from the polyline (0 is use all closest points, 1 maximum sparsity), needs to be balanced with kMaxAgentMapObservationsCount
   sampling_seed: 42 # If given, the set of scenes to sample from will be deterministic, if None, the set of scenes will be random
   obs_radius: 50.0 # Visibility radius of the agents
+  view_cone_half_angle: 1.047 # pi/3 => 120 degree total view field
+  view_occlude_objects: true
   action_space_steer_disc: 13
   action_space_accel_disc: 7
+  action_space_head_tilt_disc: 7
+  vehicle_steer_range: [-1.57, 1.57]  # pi/2 = 1.57, pi/3 = 1.05
+  vehicle_accel_range: [-4.0, 4.0]
+  head_tilt_action_range: [-0.7854, 0.7854] # radians (±45°)
   init_steps: 0 # Warmup steps
   goal_achieved_weight: 0.0
 
 wandb:
   entity: ""
   project: "humanlike"
-  group: "debug_mini"
+  group: ""
   mode: "online" # Options: online, offline, disabled
   tags: ["ppo", "ff"]
 
diff --git a/baselines/ppo/ppo_guided_autonomy.py b/baselines/ppo/ppo_guided_autonomy.py
index 13383892e..a9569bb25 100644
--- a/baselines/ppo/ppo_guided_autonomy.py
+++ b/baselines/ppo/ppo_guided_autonomy.py
@@ -146,6 +146,17 @@ def run(
     smoothen_trajectory: Annotated[Optional[int], typer.Option(help="0 or 1")] = None,
     guidance_dropout_prob: Annotated[Optional[float], typer.Option(help="The dropout probability for the guidance")] = None,
 
+    action_space_steer_disc: Annotated[Optional[int], typer.Option(help="Discretization for steer action")] = None,
+    action_space_accel_disc: Annotated[Optional[int], typer.Option(help="Discretization for acceleration action")] = None,
+    action_space_head_tilt_disc: Annotated[Optional[int], typer.Option(help="Discretization for head tilt action")] = None,
+
+    vehicle_steer_range: Annotated[Optional[list[float]], typer.Option(help="The vehicle steer range, e.g. --vehicle-steer-range -1.57 --vehicle-steer-range 1.57")] = None,
+    vehicle_accel_range: Annotated[Optional[list[float]], typer.Option(help="The vehicle accel range, e.g. --vehicle-accel-range -4.0 --vehicle-accel-range 4.0")] = None,
+    head_tilt_action_range: Annotated[Optional[list[float]], typer.Option(help="The head tilt action range, e.g. --head-tilt-action-range -0.7854 --head-tilt-action-range 0.7854")] = None,
+    
+    view_cone_half_angle: Annotated[Optional[float], typer.Option(help="The half angle for the view cone")] = None,
+    view_occlude_objects: Annotated[Optional[int], typer.Option(help="Whether to occlude objects in view; 0 or 1")] = None,
+
     smoothness_weight: Annotated[Optional[float], typer.Option(help="Scale for realism rewards")] = None,
     dist_to_goal_threshold: Annotated[Optional[float], typer.Option(help="The distance threshold for goal-achieved")] = None,
     randomize_rewards: Annotated[Optional[int], typer.Option(help="If reward_type == reward_conditioned, choose the condition_mode; 0 or 1")] = 0,
@@ -209,8 +220,16 @@ def run(
         "dist_to_goal_threshold": dist_to_goal_threshold,
         "sampling_seed": sampling_seed,
         "obs_radius": obs_radius,
+        "view_cone_half_angle": view_cone_half_angle,
+        "view_occlude_objects": None if view_occlude_objects is None else bool(view_occlude_objects),
         "collision_behavior": collision_behavior,
         "guidance_dropout_prob": guidance_dropout_prob,
+        "action_space_steer_disc": action_space_steer_disc,
+        "action_space_accel_disc": action_space_accel_disc,
+        "action_space_head_tilt_disc": action_space_head_tilt_disc,
+        "vehicle_steer_range": vehicle_steer_range,
+        "vehicle_accel_range": vehicle_accel_range,
+        "head_tilt_action_range": head_tilt_action_range,
         "remove_non_vehicles": None
         if remove_non_vehicles is None
         else bool(remove_non_vehicles),
diff --git a/gpudrive/env/env_puffer.py b/gpudrive/env/env_puffer.py
index 341fa22dd..4fa9c2c5e 100644
--- a/gpudrive/env/env_puffer.py
+++ b/gpudrive/env/env_puffer.py
@@ -259,7 +259,7 @@ def __init__(
         self.total_agents = self.max_cont_agents_per_env * self.num_worlds
 
         # Set working directory to the base directory 'gpudrive'
-        working_dir = os.path.join(Path.cwd(), "../gpudrive-emerge")
+        working_dir = os.path.join(Path.cwd(), "../gpudrive")
         os.chdir(working_dir)
 
         # Make env
diff --git a/gpudrive/networks/agents.py b/gpudrive/networks/agents.py
index 65af8191c..9f3e53736 100644
--- a/gpudrive/networks/agents.py
+++ b/gpudrive/networks/agents.py
@@ -125,6 +125,55 @@ def __init__(
             layer_init(nn.Linear(64, action_dim), std=0.01),
         )
 
+    @classmethod
+    def load_from_local_checkpoint(
+        cls, checkpoint_path, map_location="cpu", **kwargs
+    ):
+        """
+        Loads an Agent model from a local .pt checkpoint file.
+        This is designed to work with checkpoints created by `baselines/ppo/ppo_guided_autonomy.py`.
+
+        Args:
+            checkpoint_path (str): Path to the .pt file.
+            map_location (str or torch.device): Specifies how to remap storage locations.
+                                                Defaults to 'cpu'.
+            **kwargs: Additional arguments for the Agent constructor
+                      (e.g., act_func, dropout, top_k).
+                      These will override the default values in the constructor if provided.
+        """
+        # Load the checkpoint file
+        # Setting weights_only=False can be a security risk. Only use with trusted checkpoints.
+        saved_cpt = torch.load(
+            checkpoint_path, map_location=map_location, weights_only=False
+        )
+
+        # Extract parameters for model instantiation from the checkpoint
+        config = saved_cpt["config"]
+        embed_dim = saved_cpt["model_arch"]["embed_dim"]
+        action_dim = saved_cpt["action_dim"]
+
+        # Instantiate the model
+        model = cls(
+            config=config,
+            embed_dim=embed_dim,
+            action_dim=action_dim,
+            **kwargs,
+        )
+
+        # Load the state dict from the checkpoint file
+        if "parameters" in saved_cpt:
+            model.load_state_dict(saved_cpt["parameters"])
+        elif "model_state_dict" in saved_cpt:
+            model.load_state_dict(saved_cpt["model_state_dict"])
+        elif "state_dict" in saved_cpt:
+            model.load_state_dict(saved_cpt["state_dict"])
+        else:
+            # Assume the checkpoint file directly contains the model's state_dict
+            model.load_state_dict(saved_cpt)
+
+        model.eval()  # Set the model to evaluation mode
+        return model
+
     def forward(self, x, action=None):
         """Forward pass through the network.
         Args:

From 0b674cb133efa80642915cc5dd1413d825e8d27b Mon Sep 17 00:00:00 2001
From: Robin Sieber <rjs02@hotmail.com>
Date: Sun, 8 Jun 2025 13:26:33 +0200
Subject: [PATCH 33/38] set full view and no occlusion as default

---
 baselines/ppo/config/ppo_guided_autonomy.yaml | 6 +++---
 gpudrive/env/config.py                        | 4 ++--
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/baselines/ppo/config/ppo_guided_autonomy.yaml b/baselines/ppo/config/ppo_guided_autonomy.yaml
index 1e11cd4e7..ad1c4a543 100644
--- a/baselines/ppo/config/ppo_guided_autonomy.yaml
+++ b/baselines/ppo/config/ppo_guided_autonomy.yaml
@@ -42,11 +42,11 @@ environment: # Overrides default environment configs (see pygpudrive/env/config.
   polyline_reduction_threshold: 0.1 # Rate at which to sample points from the polyline (0 is use all closest points, 1 maximum sparsity), needs to be balanced with kMaxAgentMapObservationsCount
   sampling_seed: 42 # If given, the set of scenes to sample from will be deterministic, if None, the set of scenes will be random
   obs_radius: 50.0 # Visibility radius of the agents
-  view_cone_half_angle: 1.047 # pi/3 => 120 degree total view field
-  view_occlude_objects: true
+  view_cone_half_angle: 3.14159 # -> 360° total view field
+  view_occlude_objects: false
   action_space_steer_disc: 13
   action_space_accel_disc: 7
-  action_space_head_tilt_disc: 7
+  action_space_head_tilt_disc: 1
   vehicle_steer_range: [-1.57, 1.57]  # pi/2 = 1.57, pi/3 = 1.05
   vehicle_accel_range: [-4.0, 4.0]
   head_tilt_action_range: [-0.7854, 0.7854] # radians (±45°)
diff --git a/gpudrive/env/config.py b/gpudrive/env/config.py
index cb525a164..dba105886 100755
--- a/gpudrive/env/config.py
+++ b/gpudrive/env/config.py
@@ -57,7 +57,7 @@ class EnvConfig:
     # Road observation algorithm settings
     road_obs_algorithm: str = "linear"  # Algorithm for road observations
     obs_radius: float = 50.0  # Radius for road observations
-    view_cone_half_angle: float = torch.pi / 3  # Half-angle for view cone setting
+    view_cone_half_angle: float = torch.pi  # Half-angle for view cone setting
     view_occlude_objects: bool = False  # True: Vehicles are removed from observations if they are occluded by other vehicles
     polyline_reduction_threshold: float = (
         0.1  # Threshold for polyline reduction
@@ -90,7 +90,7 @@ class EnvConfig:
     # Classic or Invertible Bicycle dynamics model
     action_space_steer_disc: int = 13
     action_space_accel_disc: int = 7
-    action_space_head_tilt_disc: int = 7
+    action_space_head_tilt_disc: int = 1
 
     # Delta Local dynamics model
     dx: torch.Tensor = torch.round(torch.linspace(-2.0, 2.0, 20), decimals=3)

From 39aa469dc7e0d70c8599bab302b82fca0565734a Mon Sep 17 00:00:00 2001
From: Robin Sieber <rjs02@hotmail.com>
Date: Sun, 8 Jun 2025 13:41:36 +0200
Subject: [PATCH 34/38] add addtional sampling points for occlusion check

---
 src/consts.hpp |  4 +++
 src/sim.cpp    | 72 ++++++++++++++++++++++++++++++++++++++------------
 2 files changed, 59 insertions(+), 17 deletions(-)

diff --git a/src/consts.hpp b/src/consts.hpp
index edaa5f46b..1118aff1c 100644
--- a/src/consts.hpp
+++ b/src/consts.hpp
@@ -48,6 +48,10 @@ inline constexpr float lidarAngle = madrona::math::pi / 3; // The angle between
 // Bev observation constants
 inline constexpr int bev_rasterization_resolution = 200;
 
+// Number of points to sample on each edge of a bounding box for occlusion checks.
+// If set to 0, only corners of AABB are checked. If set to 1, midpoints of edges are also checked etc.
+inline constexpr madrona::CountT kNumOcclusionEdgeSamples = 1;
+
 // Time (seconds) per step
 inline constexpr float deltaT = 0.04f;
 
diff --git a/src/sim.cpp b/src/sim.cpp
index 48d773ca4..2e4279fcf 100755
--- a/src/sim.cpp
+++ b/src/sim.cpp
@@ -331,21 +331,58 @@ inline CountT filterByOcclusionAll(Engine &ctx,
             {-half_width,  half_length,  half_height}, { half_width,  half_length,  half_height}
         };
 
-        int unoccluded_corner_rays = 0;
+        constexpr CountT num_points_to_check = 8 + 12 * consts::kNumOcclusionEdgeSamples;
+        madrona::math::Vector3 local_points_to_check[num_points_to_check];
+        CountT point_idx = 0;
 
-        for (int i = 0; i < 8; ++i) { // check all corners
-            Vector3 target_corner_world = target_world_center + target_rot_comp.rotateVec(local_corners[i]);
+        for (int i = 0; i < 8; ++i) {
+            local_points_to_check[point_idx++] = local_corners[i];
+        }
+
+        // add points on edge to check if config is set
+        if constexpr (consts::kNumOcclusionEdgeSamples > 0) {
+            // sample uniformly along edge
+            auto add_edge_points = [&](const Vector3& start, const Vector3& end) {
+                for (CountT i = 1; i <= consts::kNumOcclusionEdgeSamples; ++i) {
+                    float t = (float)i / (float)(consts::kNumOcclusionEdgeSamples + 1);
+                    local_points_to_check[point_idx++] = start + t * (end - start);
+                }
+            };
+
+            // Edges along x
+            add_edge_points(local_corners[0], local_corners[1]);
+            add_edge_points(local_corners[2], local_corners[3]);
+            add_edge_points(local_corners[4], local_corners[5]);
+            add_edge_points(local_corners[6], local_corners[7]);
+
+            // Edges along y
+            add_edge_points(local_corners[0], local_corners[2]);
+            add_edge_points(local_corners[1], local_corners[3]);
+            add_edge_points(local_corners[4], local_corners[6]);
+            add_edge_points(local_corners[5], local_corners[7]);
+
+            // Edges along z
+            add_edge_points(local_corners[0], local_corners[4]);
+            add_edge_points(local_corners[1], local_corners[5]);
+            add_edge_points(local_corners[2], local_corners[6]);
+            add_edge_points(local_corners[3], local_corners[7]);
+        }
+
+        bool is_visible = false;
+
+        for (CountT i = 0; i < num_points_to_check; ++i) { // check all points
+            Vector3 target_point_world = target_world_center + target_rot_comp.rotateVec(local_points_to_check[i]);
             
-            Vector3 vec_observer_to_target_corner = target_corner_world - observer_pos_3d;
-            float dist_to_target_corner = vec_observer_to_target_corner.length();
+            Vector3 vec_observer_to_target_point = target_point_world - observer_pos_3d;
+            float dist_to_target_point = vec_observer_to_target_point.length();
 
-            if (dist_to_target_corner < 0.1f) { // add to visible if really close
-                unoccluded_corner_rays++;
+            if (dist_to_target_point < 0.1f) { // add to visible if really close
+                is_visible = true;
                 break; // early return since one clear ray is enough
             }
-            Vector3 normalized_ray_dir_to_target_corner = vec_observer_to_target_corner / dist_to_target_corner;
+            Vector3 normalized_ray_dir_to_target_point = vec_observer_to_target_point / dist_to_target_point;
 
-            bool corner_ray_occluded = false;
+            bool point_ray_occluded = false;
             for (CountT occluder_idx = 0; occluder_idx < objects_to_filter.size(); ++occluder_idx) {
                 Entity potential_occluder_entity = objects_to_filter[occluder_idx];
                 if (potential_occluder_entity == target_entity) {
@@ -375,23 +412,24 @@ inline CountT filterByOcclusionAll(Engine &ctx,
 
                 // inverse ray direction
                 Diag3x3 inv_ray_d = {
-                    (normalized_ray_dir_to_target_corner.x == 0.f) ? copysignf(FLT_MAX, normalized_ray_dir_to_target_corner.x) : 1.f / normalized_ray_dir_to_target_corner.x,
-                    (normalized_ray_dir_to_target_corner.y == 0.f) ? copysignf(FLT_MAX, normalized_ray_dir_to_target_corner.y) : 1.f / normalized_ray_dir_to_target_corner.y,
-                    (normalized_ray_dir_to_target_corner.z == 0.f) ? copysignf(FLT_MAX, normalized_ray_dir_to_target_corner.z) : 1.f / normalized_ray_dir_to_target_corner.z
+                    (normalized_ray_dir_to_target_point.x == 0.f) ? copysignf(FLT_MAX, normalized_ray_dir_to_target_point.x) : 1.f / normalized_ray_dir_to_target_point.x,
+                    (normalized_ray_dir_to_target_point.y == 0.f) ? copysignf(FLT_MAX, normalized_ray_dir_to_target_point.y) : 1.f / normalized_ray_dir_to_target_point.y,
+                    (normalized_ray_dir_to_target_point.z == 0.f) ? copysignf(FLT_MAX, normalized_ray_dir_to_target_point.z) : 1.f / normalized_ray_dir_to_target_point.z
                 };
 
                 float hit_t_occluder = 0.f; 
-                if (world_occluder_aabb.rayIntersects(observer_pos_3d, inv_ray_d, 0.01f, dist_to_target_corner - 0.01f, hit_t_occluder)) {
-                    corner_ray_occluded = true;
+                if (world_occluder_aabb.rayIntersects(observer_pos_3d, inv_ray_d, 0.01f, dist_to_target_point - 0.01f, hit_t_occluder)) {
+                    point_ray_occluded = true;
                     break; 
                 }
             }
-            if (!corner_ray_occluded) {
-                unoccluded_corner_rays++;
+            if (!point_ray_occluded) {
+                is_visible = true;
+                break;
             }
         }
 
-        if (unoccluded_corner_rays > 0) {
+        if (is_visible) {
             objects_to_filter[visible_count++] = target_entity;
         }
     }

From aecae0c32bde99ee04ac397559aaf7ff7857b8e8 Mon Sep 17 00:00:00 2001
From: Robin Sieber <rjs02@hotmail.com>
Date: Sun, 8 Jun 2025 21:42:23 +0200
Subject: [PATCH 35/38] rename parameter and add documentation

---
 baselines/ppo/ppo_guided_autonomy.py |  4 +--
 gpudrive/env/README.md               | 39 +++++++++++++++++++++++++---
 gpudrive/env/base_env.py             |  2 +-
 gpudrive/env/config.py               |  2 +-
 src/bindings.cpp                     |  2 +-
 src/init.hpp                         |  2 +-
 src/sim.cpp                          |  6 ++---
 7 files changed, 45 insertions(+), 12 deletions(-)

diff --git a/baselines/ppo/ppo_guided_autonomy.py b/baselines/ppo/ppo_guided_autonomy.py
index a9569bb25..1ed73e57d 100644
--- a/baselines/ppo/ppo_guided_autonomy.py
+++ b/baselines/ppo/ppo_guided_autonomy.py
@@ -155,7 +155,7 @@ def run(
     head_tilt_action_range: Annotated[Optional[list[float]], typer.Option(help="The head tilt action range, e.g. --head-tilt-action-range -0.7854 --head-tilt-action-range 0.7854")] = None,
     
     view_cone_half_angle: Annotated[Optional[float], typer.Option(help="The half angle for the view cone")] = None,
-    view_occlude_objects: Annotated[Optional[int], typer.Option(help="Whether to occlude objects in view; 0 or 1")] = None,
+    remove_occluded_agents: Annotated[Optional[int], typer.Option(help="Whether to occlude objects in view; 0 or 1")] = None,
 
     smoothness_weight: Annotated[Optional[float], typer.Option(help="Scale for realism rewards")] = None,
     dist_to_goal_threshold: Annotated[Optional[float], typer.Option(help="The distance threshold for goal-achieved")] = None,
@@ -221,7 +221,7 @@ def run(
         "sampling_seed": sampling_seed,
         "obs_radius": obs_radius,
         "view_cone_half_angle": view_cone_half_angle,
-        "view_occlude_objects": None if view_occlude_objects is None else bool(view_occlude_objects),
+        "remove_occluded_agents": None if remove_occluded_agents is None else bool(remove_occluded_agents),
         "collision_behavior": collision_behavior,
         "guidance_dropout_prob": guidance_dropout_prob,
         "action_space_steer_disc": action_space_steer_disc,
diff --git a/gpudrive/env/README.md b/gpudrive/env/README.md
index 2febd2ade..1cb783489 100755
--- a/gpudrive/env/README.md
+++ b/gpudrive/env/README.md
@@ -59,10 +59,30 @@ For instance, with `dynamics_model: str = "classic"`, the default action space i
 
 ```Python
 # Action space (joint discrete)
-head_tilt_actions: torch.Tensor = torch.Tensor([0])
-steer_actions: torch.Tensor = torch.round(torch.linspace(-1.0, 1.0, 13), decimals=3)
-accel_actions: torch.Tensor = torch.round(torch.linspace(-3, 3, 7), decimals=3)
+action_space_steer_disc: int = 13
+action_space_accel_disc: int = 7
+action_space_head_tilt_disc: int = 1
+
+# Type-aware action space settings
+use_type_aware_actions: bool = True  # Toggles type-aware action mapping: if False, use vehicle ranges for all agents
+
+# Vehicle action ranges
+vehicle_accel_range: Tuple[float, float] = (-4.0, 4.0)  # m/s²
+vehicle_steer_range: Tuple[float, float] = (-1.57, 1.57)  # radians
+
+# Cyclist action ranges
+cyclist_accel_range: Tuple[float, float] = (-2.5, 2.5)    # m/s²
+cyclist_steer_range: Tuple[float, float] = (-2.09, 2.09)  # radians (±120°)
+
+# Pedestrian action ranges
+pedestrian_accel_range: Tuple[float, float] = (-1.5, 1.5)  # m/s²
+pedestrian_steer_range: Tuple[float, float] = (-3.14, 3.14)  # radians (±180°)
+
+# Head tilt action range
+head_tilt_action_range: Tuple[float, float] = (-0.7854, 0.7854)  # radians (±45°)
 ```
+The head tilt action is relevant when the view cone is not 360° (see below).
+
 
 ### Continuous
 
@@ -89,6 +109,19 @@ lidar_obs: bool = True # Use LiDAR data
 
 ---
 
+### Restriced Observation Space
+
+By default, _every_ road segment and agent within a radius of 50m of the ego is observable and added to partner_obs and road_map_obs, respectively.
+
+The observation can be restriced in various ways:
+- **smaller observation radius**: By default the radius is set to 50m. This can be changed using the `obs_radius` option.
+
+- **smaller view cone**: Instead of observing 360° around the vehicle, the view can be restricted to a cone using the `view_cone_half_angle` config parameter. By default the _half_ angle is set to $\pi = 180°$, i.e. a full 360° view. Using the head tilt action, the heading of the view angle can be changed (within the given range) without changing the heading of the vehicle (the direction of driving). Relevant parameters: `view_cone_half_angle`, `action_space_head_tilt_disc`, `head_tilt_action_range`
+
+
+- **remove agents that are occluded**: Agents that are not technically not visible because they are hidden behind other agents are not added to partner_obs if `remove_occluded_agents = True`. This is determined by casting rays: If one ray hits the vehicle without occlusion, the agent is considered visible. By default, the 8 corners as well as the midpoints of each edge of the bounding box are checked. The number of sampled points on the edge of the bounding box can be set in `consts.hpp`
+
+
 ### Data Structures
 
 For detailed structures, refer to:
diff --git a/gpudrive/env/base_env.py b/gpudrive/env/base_env.py
index 7034a766d..755883674 100755
--- a/gpudrive/env/base_env.py
+++ b/gpudrive/env/base_env.py
@@ -87,7 +87,7 @@ def _set_road_reduction_params(self, params):
         """
         params.observationRadius = self.config.obs_radius
         params.viewConeHalfAngle = self.config.view_cone_half_angle
-        params.viewOccludeObjects = self.config.view_occlude_objects
+        params.removeOccludedAgents = self.config.remove_occluded_agents
         if self.config.road_obs_algorithm == "k_nearest_roadpoints":
             params.roadObservationAlgorithm = (
                 madrona_gpudrive.FindRoadObservationsWith.KNearestEntitiesWithRadiusFiltering
diff --git a/gpudrive/env/config.py b/gpudrive/env/config.py
index dba105886..c5cabec58 100755
--- a/gpudrive/env/config.py
+++ b/gpudrive/env/config.py
@@ -58,7 +58,7 @@ class EnvConfig:
     road_obs_algorithm: str = "linear"  # Algorithm for road observations
     obs_radius: float = 50.0  # Radius for road observations
     view_cone_half_angle: float = torch.pi  # Half-angle for view cone setting
-    view_occlude_objects: bool = False  # True: Vehicles are removed from observations if they are occluded by other vehicles
+    remove_occluded_agents: bool = False  # True: Vehicles are removed from observations if they are occluded by other vehicles
     polyline_reduction_threshold: float = (
         0.1  # Threshold for polyline reduction
     )
diff --git a/src/bindings.cpp b/src/bindings.cpp
index 9dcf17025..dbcb0a1da 100755
--- a/src/bindings.cpp
+++ b/src/bindings.cpp
@@ -51,7 +51,7 @@ namespace madrona_gpudrive
             .def_rw("polylineReductionThreshold", &Parameters::polylineReductionThreshold)
             .def_rw("observationRadius", &Parameters::observationRadius)
             .def_rw("viewConeHalfAngle", &Parameters::viewConeHalfAngle)
-            .def_rw("viewOccludeObjects", &Parameters::viewOccludeObjects)
+            .def_rw("removeOccludedAgents", &Parameters::removeOccludedAgents)
             .def_rw("rewardParams", &Parameters::rewardParams)
             .def_rw("collisionBehaviour", &Parameters::collisionBehaviour)
             .def_rw("goalBehaviour", &Parameters::goalBehaviour)
diff --git a/src/init.hpp b/src/init.hpp
index c72522bec..cd1e1ea38 100755
--- a/src/init.hpp
+++ b/src/init.hpp
@@ -120,7 +120,7 @@ namespace madrona_gpudrive
         float polylineReductionThreshold;
         float observationRadius;
         float viewConeHalfAngle;
-        bool viewOccludeObjects;
+        bool removeOccludedAgents;
         RewardParams rewardParams;
         CollisionBehaviour collisionBehaviour = CollisionBehaviour::AgentStop; // Default: AgentStop
         GoalBehaviour goalBehaviour = GoalBehaviour::Remove;  // Default to current behavior
diff --git a/src/sim.cpp b/src/sim.cpp
index 2e4279fcf..b6d460336 100755
--- a/src/sim.cpp
+++ b/src/sim.cpp
@@ -447,7 +447,7 @@ inline void collectPartnerObsSystem(Engine &ctx,
         return;
 
     // User config
-    // viewRadius (float), viewConeHalfAngle (float), viewOccludeObjects (bool; False = no filtering), 
+    // viewRadius (float), viewConeHalfAngle (float), removeOccludedAgents (bool; False = no filtering), 
 
     // to write partner features into array
     auto &partner_obs = ctx.get<PartnerObservations>(agent_iface.e);
@@ -466,7 +466,7 @@ inline void collectPartnerObsSystem(Engine &ctx,
     Span<Entity> objectsDynamic(objectsDynamicBuffer, dynamicCount);
         
     // narrow phase: raycast to get occlusion by other dynamic objects
-    if (ctx.data().params.viewOccludeObjects) {
+    if (ctx.data().params.removeOccludedAgents) {
         dynamicCount = filterByOcclusionAll(ctx, vf, objectsDynamic);
     }
 
@@ -516,7 +516,7 @@ inline void collectMapObservationsSystem(Engine &ctx,
     auto &map_obs = ctx.get<AgentMapObservations>(agent_iface.e);
 
     // User config (some of it not implemented yet, but assume it will be): 
-    // viewRadius (float), viewConeHalfAngle (float), viewOccludeObjects (bool; False = no filtering), 
+    // viewRadius (float), viewConeHalfAngle (float), removeOccludedAgents (bool; False = no filtering), 
 
     // Get agent's heading from rotation
     float heading = utils::quatToYaw(rot);

From f492dd0c40a0416d1ba52db64932630e15b6095a Mon Sep 17 00:00:00 2001
From: Robin Sieber <rjs02@hotmail.com>
Date: Fri, 13 Jun 2025 01:20:19 +0200
Subject: [PATCH 36/38] pass config parameters into config object

---
 gpudrive/env/env_puffer.py | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)

diff --git a/gpudrive/env/env_puffer.py b/gpudrive/env/env_puffer.py
index 4fa9c2c5e..7c24021b8 100644
--- a/gpudrive/env/env_puffer.py
+++ b/gpudrive/env/env_puffer.py
@@ -168,6 +168,15 @@ def __init__(
         dynamics_model="classic",
         action_space_steer_disc=13,
         action_space_accel_disc=7,
+        action_space_head_tilt_disc=1,
+        use_type_aware_actions=True,
+        vehicle_accel_range=(-4.0, 4.0),
+        vehicle_steer_range=(-1.57, 1.57),
+        cyclist_accel_range=(-2.5, 2.5),
+        cyclist_steer_range=(-2.09, 2.09),
+        pedestrian_accel_range=(-1.5, 1.5),
+        pedestrian_steer_range=(-3.14, 3.14),
+        head_tilt_action_range=(-0.7854, 0.7854),
         ego_state=True,
         road_map_obs=True,
         partner_obs=True,
@@ -196,6 +205,8 @@ def __init__(
         guidance_dropout_prob=0.0,
         remove_non_vehicles=False,
         obs_radius=50.0,
+        view_cone_half_angle=3.14159,
+        remove_occluded_agents=False,
         track_realism_metrics=False,
         track_n_worlds=2,
         render=False,
@@ -293,6 +304,18 @@ def __init__(
             obs_radius=obs_radius,
             action_space_steer_disc=action_space_steer_disc,
             action_space_accel_disc=action_space_accel_disc,
+            action_space_head_tilt_disc=action_space_head_tilt_disc,
+            use_type_aware_actions=use_type_aware_actions,
+            vehicle_accel_range=vehicle_accel_range,
+            vehicle_steer_range=vehicle_steer_range,
+            cyclist_accel_range=cyclist_accel_range,
+            cyclist_steer_range=cyclist_steer_range,
+            pedestrian_accel_range=pedestrian_accel_range,
+            pedestrian_steer_range=pedestrian_steer_range,
+            head_tilt_action_range=head_tilt_action_range,
+            view_cone_half_angle=view_cone_half_angle,
+            remove_occluded_agents=remove_occluded_agents,
+            **{k: v for k, v in kwargs.items() if hasattr(EnvConfig, k)}
         )
         render_config = RenderConfig(
             render_3d=render_3d,

From b705eb88c9654a4291c815241b634a4f17a9470f Mon Sep 17 00:00:00 2001
From: Daphne Cornelisse <33460159+daphne-cornelisse@users.noreply.github.com>
Date: Sun, 15 Jun 2025 22:06:56 -0700
Subject: [PATCH 37/38] Integrate traffic light states (#425)

* Add tl_states to extraction script.

* WIP

* Add back metadata

* Add tl_states struct and access through simulator.

* Add time index

* Cleanup

* Cleanup

* SMall fix

* Add tl state data struct

* Add minimum test script

* Add minimum test script

* [Work In progress] Fixed initialization and made TL a singleton tensor

* Fix json init and export

* mean centering

* Test and omit unnesecessary code in tl obs

* Changed data access format in python code

* Added a more interpretable positional element arrays

* Unpack tl_states correctly

* Add tl state plotting function in visualizer

* mini bug

* Fix traffic lights by exporting everything as float32

* Improve colors

* Remove test file

---------

Co-authored-by: Aarav Pandya <ap7641@nyu.edu>
Co-authored-by: Eugene Vinitsky <eugenevinitsky@users.noreply.github.com>
Co-authored-by: Pragnay Mandavilli <pm3881@ga034.hpc.nyu.edu>
Co-authored-by: Pragnay Mandavilli <pm3881@ga014.hpc.nyu.edu>
---
 ...ecord-00000-of-00150_2a173b334be3d32c.json |   1 +
 ...record-00000-of-00150_2af8a17d6cee3f8.json |   1 +
 ...ecord-00000-of-00150_2baf77fa47457d69.json |   1 +
 data_utils/process_waymo_files.py             |  17 +--
 gpudrive/datatypes/observation.py             | 141 ++++++++++++++++++
 gpudrive/env/env_torch.py                     |   8 +-
 gpudrive/visualize/core.py                    | 100 +++++++++++++
 src/bindings.cpp                              |   1 +
 src/consts.hpp                                |   1 +
 src/init.hpp                                  |   3 +
 src/json_serialization.hpp                    |  85 +++++++++++
 src/level_gen.cpp                             |  14 ++
 src/mgr.cpp                                   |  13 ++
 src/mgr.hpp                                   |   1 +
 src/sim.cpp                                   |   6 +-
 src/sim.hpp                                   |  35 ++---
 src/types.hpp                                 |  37 ++++-
 17 files changed, 429 insertions(+), 36 deletions(-)
 create mode 100644 data/processed/tl/tfrecord-00000-of-00150_2a173b334be3d32c.json
 create mode 100644 data/processed/tl/tfrecord-00000-of-00150_2af8a17d6cee3f8.json
 create mode 100644 data/processed/tl/tfrecord-00000-of-00150_2baf77fa47457d69.json

diff --git a/data/processed/tl/tfrecord-00000-of-00150_2a173b334be3d32c.json b/data/processed/tl/tfrecord-00000-of-00150_2a173b334be3d32c.json
new file mode 100644
index 000000000..87cf9bd5e
--- /dev/null
+++ b/data/processed/tl/tfrecord-00000-of-00150_2a173b334be3d32c.json
@@ -0,0 +1 @@
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diff --git a/data/processed/tl/tfrecord-00000-of-00150_2af8a17d6cee3f8.json b/data/processed/tl/tfrecord-00000-of-00150_2af8a17d6cee3f8.json
new file mode 100644
index 000000000..ccaabe782
--- /dev/null
+++ b/data/processed/tl/tfrecord-00000-of-00150_2af8a17d6cee3f8.json
@@ -0,0 +1 @@
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diff --git a/data/processed/tl/tfrecord-00000-of-00150_2baf77fa47457d69.json b/data/processed/tl/tfrecord-00000-of-00150_2baf77fa47457d69.json
new file mode 100644
index 000000000..717c4fd81
--- /dev/null
+++ b/data/processed/tl/tfrecord-00000-of-00150_2baf77fa47457d69.json
@@ -0,0 +1 @@
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73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90], "lane_id": [479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479, 479]}, "480": {"state": ["stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", "stop", 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68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90], "lane_id": [480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480, 480]}}, "metadata": {"sdc_track_index": 110, "objects_of_interest": [], "tracks_to_predict": [{"track_index": 7, "difficulty": 1}, {"track_index": 4, "difficulty": 1}, {"track_index": 10, "difficulty": 1}, {"track_index": 20, "difficulty": 1}]}}
diff --git a/data_utils/process_waymo_files.py b/data_utils/process_waymo_files.py
index 6bf3f8936..c8f55f91a 100644
--- a/data_utils/process_waymo_files.py
+++ b/data_utils/process_waymo_files.py
@@ -168,6 +168,7 @@ def _init_tl_object(mapstate: scenario_pb2.DynamicMapState) -> Dict[int, Any]:
             "x": lane_state.stop_point.x,
             "y": lane_state.stop_point.y,
             "z": lane_state.stop_point.z,
+            "lane_id": lane_state.lane,
         }
     return returned_dict
 
@@ -327,20 +328,16 @@ def waymo_to_scenario(
 
     # Construct the traffic light states
     tl_dict = defaultdict(
-        lambda: {"state": [], "x": [], "y": [], "z": [], "time_index": []}
+        lambda: {"state": [], "x": [], "y": [], "z": [], "time_index": [], "lane_id": []}
     )
     all_keys = ["state", "x", "y", "z"]
-    i = 0
-    for dynamic_map_state in protobuf.dynamic_map_states:
+    for i, dynamic_map_state in enumerate(protobuf.dynamic_map_states):
         traffic_light_dict = _init_tl_object(dynamic_map_state)
-        # there is a traffic light but we don't want traffic light scenes so just return
-        if len(traffic_light_dict) > 0:
-            return
-        for id, value in traffic_light_dict.items():
+        for lane_id, value in traffic_light_dict.items():
             for key in all_keys:
-                tl_dict[id][key].append(value[key])
-            tl_dict[id]["time_index"].append(i)
-        i += 1
+                tl_dict[lane_id][key].append(value[key])
+            tl_dict[lane_id]["time_index"].append(i)
+            tl_dict[lane_id]["lane_id"].append(lane_id)
 
     # Construct the map states
     roads = []
diff --git a/gpudrive/datatypes/observation.py b/gpudrive/datatypes/observation.py
index 0573cc25c..b545b3a4e 100644
--- a/gpudrive/datatypes/observation.py
+++ b/gpudrive/datatypes/observation.py
@@ -366,3 +366,144 @@ def one_hot_encode_bev_map(self):
             self.bev_segmentation_map.long(),
             num_classes=constants.NUM_MADRONA_ENTITY_TYPES,  # From size of Madrona EntityType
         )
+
+
+@dataclass
+class TrafficLightObs:
+    """
+    A dataclass that represents traffic light information in the scenario.
+
+    This data struct contains the time series of traffic light information.
+    It contains the state (unknown: 0, stop: 1, caution: 2, go: 3), position, and lane_ids.
+
+    Initialized from tl_states_tensor (from Manager.trafficLightTensor()).
+    For details, see `TrafficLightState` in src/types.hpp.
+    Shape: (num_worlds, max_traffic_lights, num_timesteps * features).
+
+    Attributes:
+        state: The state of each traffic light (0=unknown, 1=stop, 2=caution, 3=go)
+        pos_x: X-coordinate of the traffic light
+        pos_y: Y-coordinate of the traffic light
+        pos_z: Z-coordinate of the traffic light
+        time_index: Time index of the traffic light state
+        lane_id: Lane ID associated with the traffic light
+        valid_mask: Boolean mask indicating valid traffic lights
+    """
+
+    state: torch.Tensor
+    pos_x: torch.Tensor
+    pos_y: torch.Tensor
+    pos_z: torch.Tensor
+    time_index: torch.Tensor
+    lane_id: torch.Tensor
+    valid_mask: torch.Tensor
+
+    def __init__(
+        self,
+        tl_states_tensor: torch.Tensor,
+
+    ):
+        """Initializes the traffic light observation from a tensor."""
+        traj_length = constants.LOG_TRAJECTORY_LENGTH
+
+        # Calculate indices based on C++ struct layout:
+        # laneId (1) + state[traj_length] + x[traj_length] + y[traj_length] + z[traj_length] + timeIndex[traj_length] + numStates (1)
+
+        lane_id_end_idx = 1
+        state_end_idx = lane_id_end_idx + traj_length
+        pos_x_end_idx = state_end_idx + traj_length
+        pos_y_end_idx = pos_x_end_idx + traj_length
+        pos_z_end_idx = pos_y_end_idx + traj_length
+        time_index_end_idx = pos_z_end_idx + traj_length
+
+        # Extract fields according to C++ struct layout
+        # See `TrafficLightState` in src/types.hpp for details
+        self.lane_id = tl_states_tensor[:, :, 0]  # Single lane ID value
+        self.state = tl_states_tensor[
+            :, :, lane_id_end_idx:state_end_idx
+        ]  # state[traj_length]
+        self.pos_x = tl_states_tensor[
+            :, :, state_end_idx:pos_x_end_idx
+        ].float()  # x[traj_length]
+        self.pos_y = tl_states_tensor[
+            :, :, pos_x_end_idx:pos_y_end_idx
+        ].float()  # y[traj_length]
+        self.pos_z = tl_states_tensor[
+            :, :, pos_y_end_idx:pos_z_end_idx
+        ].float()  # z[traj_length]
+        self.time_index = tl_states_tensor[
+            :, :, pos_z_end_idx:time_index_end_idx
+        ]  # timeIndex[traj_length]
+        self.num_states = tl_states_tensor[
+            :, :, time_index_end_idx
+        ]  # Single numStates value
+
+        # Create a valid mask based on numStates
+        # Traffic lights are valid if they have numStates > 0
+        self.valid_mask = self.num_states > 0
+
+    @classmethod
+    def from_tensor(
+        cls,
+        tl_states_tensor: madrona_gpudrive.madrona.Tensor,
+        backend="torch",
+        device="cuda",
+    ):
+        """Creates a TrafficLightObs from a tensor.
+
+        Args:
+            tl_states_tensor: The traffic light state tensor from the simulation
+            backend: Which backend to use ("torch" or "jax")
+            device: The device to place tensors on
+
+        Returns:
+            A TrafficLightObs instance
+        """
+        if backend == "torch":
+            tensor = tl_states_tensor.to_torch().clone().to(device)
+            obj = cls(tensor)
+            return obj
+        elif backend == "jax":
+            raise NotImplementedError("JAX backend not implemented yet.")
+
+    def normalize(self):
+        """Normalizes the traffic light observation coordinates."""
+
+        # Normalize position coordinates
+        self.pos_x = normalize_min_max(
+            tensor=self.pos_x,
+            min_val=constants.MIN_REL_COORD,
+            max_val=constants.MAX_REL_COORD,
+        )
+        self.pos_y = normalize_min_max(
+            tensor=self.pos_y,
+            min_val=constants.MIN_REL_COORD,
+            max_val=constants.MAX_REL_COORD,
+        )
+        self.pos_z = normalize_min_max(
+            tensor=self.pos_z,
+            min_val=constants.MIN_Z_COORD,
+            max_val=constants.MAX_Z_COORD,
+        )
+    def one_hot_encode_states(self):
+        """One-hot encodes the traffic light states.
+
+        Converts the state values to one-hot encoded vectors with 4 classes:
+        0: Unknown
+        1: Stop
+        2: Caution
+        3: Go
+        """
+        # Make sure values are in range 0-3
+        state_clamped = torch.clamp(self.state, 0, 3)
+        # One-hot encode
+        self.state_onehot = torch.nn.functional.one_hot(
+            state_clamped, num_classes=4
+        ) * self.valid_mask.unsqueeze(-1)
+
+        return self.state_onehot
+
+    @property
+    def shape(self) -> tuple[int, ...]:
+        """Shape: (num_worlds, max_traffic_lights, num_timesteps)."""
+        return self.state.shape
diff --git a/gpudrive/env/env_torch.py b/gpudrive/env/env_torch.py
index edc5925f3..10a139385 100755
--- a/gpudrive/env/env_torch.py
+++ b/gpudrive/env/env_torch.py
@@ -2060,7 +2060,7 @@ def render(self, focus_env_idx=0, focus_agent_idx=[0, 1]):
 
 if __name__ == "__main__":
 
-    FOCUS_AGENTS = [0, 1, 2, 3, 4]
+    FOCUS_AGENTS = [0] #[0, 1, 2, 3, 4]
 
     env_config = EnvConfig(
         guidance=True,
@@ -2079,8 +2079,8 @@ def render(self, focus_env_idx=0, focus_agent_idx=[0, 1]):
 
     # Create data loader
     train_loader = SceneDataLoader(
-        root="data/processed/wosac/validation_interactive/json",
-        batch_size=10,
+        root="data/processed/tl",
+        batch_size=1,
         dataset_size=100,
         sample_with_replacement=False,
         shuffle=False,
@@ -2116,7 +2116,7 @@ def render(self, focus_env_idx=0, focus_agent_idx=[0, 1]):
 
         obs = env.get_obs(control_mask)
         reward = env.get_rewards()
-        if time_step % 10 == 0 or time_step > env.episode_len - 3:
+        if time_step % 20 == 0 or time_step > env.episode_len - 3:
             sim_states, agent_obs = env.render(focus_agent_idx=FOCUS_AGENTS)
             sim_frames.append(img_from_fig(sim_states[0]))
             for i in FOCUS_AGENTS:
diff --git a/gpudrive/visualize/core.py b/gpudrive/visualize/core.py
index de5ec020f..fa7b2df99 100644
--- a/gpudrive/visualize/core.py
+++ b/gpudrive/visualize/core.py
@@ -24,6 +24,7 @@
     GlobalEgoState,
     PartnerObs,
     LidarObs,
+    TrafficLightObs,
 )
 from gpudrive.datatypes.trajectory import LogTrajectory, VBDTrajectory
 from gpudrive.datatypes.control import ResponseType
@@ -94,6 +95,11 @@ def initialize_static_scenario_data(
             )
 
         self.trajectory = reference_trajectory
+        self.tl_obs = TrafficLightObs.from_tensor(
+            tl_states_tensor=self.sim_object.tl_state_tensor(),
+            backend=self.backend,
+            device=self.device,
+        )
 
     def plot_simulator_state(
         self,
@@ -320,6 +326,14 @@ def plot_simulator_state(
                     plot_guidance_up_to_time=plot_guidance_up_to_time,
                 )
 
+            self._plot_traffic_lights(
+                ax=ax,
+                env_idx=env_idx,
+                tl_obs=self.tl_obs,
+                time_step=time_step if time_step is not None else 0,
+                marker_size_scale=marker_scale,
+            )
+
             # Draw the agents
             self._plot_filtered_agent_bounding_boxes(
                 ax=ax,
@@ -946,6 +960,92 @@ def _plot_vbd_trajectory(
                 zorder=0,
             )
 
+    def _plot_traffic_lights(
+        self,
+        ax: matplotlib.axes.Axes,
+        env_idx: int,
+        tl_obs: "TrafficLightObs",
+        time_step: int = 0,
+        marker_size_scale: float = 1.0,
+    ):
+        """Plot traffic light states as colored dots.
+
+        Args:
+            ax: Matplotlib axis to plot on
+            env_idx: Environment index
+            tl_obs: Traffic light observation object
+            time_step: Current time step
+            marker_size_scale: Scale factor for marker size
+        """
+
+        # Traffic light state colors
+        TL_STATE_COLORS = {
+            0: "#C5C5C5",  # Unknown - gray
+            1: "r",  # Stop - red
+            2: "tab:orange",  # Caution - orange
+            3: "g",  # Go - green
+        }
+
+        # Get valid traffic lights for this environment
+        valid_mask = tl_obs.valid_mask[env_idx, :]
+        if not valid_mask.any():
+            return
+
+        # Clamp time_step to available data
+        max_time_idx = tl_obs.state.shape[2] - 1
+        time_step = min(time_step, max_time_idx)
+
+        # Get traffic light data for valid lights at current time step
+        valid_indices = torch.where(valid_mask)[0]
+
+        for tl_idx in valid_indices:
+            # Get position (use first valid position if time series)
+            pos_x = tl_obs.pos_x[env_idx, tl_idx, time_step].item()
+            pos_y = tl_obs.pos_y[env_idx, tl_idx, time_step].item()
+
+            # Skip if position is invalid (0,0 or out of bounds)
+            if (
+                (pos_x == 0 and pos_y == 0)
+                or abs(pos_x) > 1000
+                or abs(pos_y) > 1000
+            ):
+                continue
+
+            # Get current state
+            state = int(tl_obs.state[env_idx, tl_idx, time_step].item())
+            state = max(0, min(3, state))  # Clamp to valid range
+
+            color = TL_STATE_COLORS[state]
+
+            if self.render_3d:
+                # Plot as elevated marker in 3D
+                height = 0.2  # Height above ground for visibility
+                ax.scatter3D(
+                    [pos_x],
+                    [pos_y],
+                    [height],
+                    color=color,
+                    s=60 * marker_size_scale,
+                    marker="o",
+                    edgecolors="black",
+                    linewidth=0.5,
+                    alpha=0.8,
+                    zorder=10,
+                )
+            else:
+                # Plot as 2D marker
+                ax.scatter(
+                    pos_x,
+                    pos_y,
+                    color=color,
+                    s=30 * marker_size_scale,
+                    marker="o",
+                    edgecolors="black",
+                    linewidth=0.5,
+                    alpha=0.9,
+                    zorder=10,
+                )
+
     def _get_endpoints(self, x, y, length, yaw):
         """Compute the start and end points of a road segment."""
         center = np.array([x, y])
diff --git a/src/bindings.cpp b/src/bindings.cpp
index dbcb0a1da..8196e4695 100755
--- a/src/bindings.cpp
+++ b/src/bindings.cpp
@@ -143,6 +143,7 @@ namespace madrona_gpudrive
             .def("set_maps", &Manager::setMaps)
             .def("world_means_tensor", &Manager::worldMeansTensor)
             .def("metadata_tensor", &Manager::metadataTensor)
+            .def("tl_state_tensor", &Manager::trafficLightTensor)
             .def("vbd_trajectory_tensor", &Manager::vbdTrajectoryTensor)
             .def("map_name_tensor", &Manager::mapNameTensor)
             .def("deleteAgents", [](Manager &self, nb::dict py_agents_to_delete) {
diff --git a/src/consts.hpp b/src/consts.hpp
index 1118aff1c..b40e31325 100644
--- a/src/consts.hpp
+++ b/src/consts.hpp
@@ -11,6 +11,7 @@ namespace consts {
 inline constexpr madrona::CountT kMaxAgentCount = 32;
 inline constexpr madrona::CountT kMaxRoadEntityCount = 10000;
 inline constexpr madrona::CountT kMaxAgentMapObservationsCount = 186;
+inline constexpr madrona::CountT kMaxTrafficLightCount = 16;
 
 inline constexpr bool useEstimatedYaw = true;
 
diff --git a/src/init.hpp b/src/init.hpp
index cd1e1ea38..a848e35a1 100755
--- a/src/init.hpp
+++ b/src/init.hpp
@@ -55,10 +55,13 @@ namespace madrona_gpudrive
     {
         MapObject objects[MAX_OBJECTS];
         MapRoad roads[MAX_ROADS];
+        TrafficLightState trafficLightStates[consts::kMaxTrafficLightCount]; 
 
         uint32_t numObjects;
         uint32_t numRoads;
         uint32_t numRoadSegments;
+        uint32_t numTrafficLights;  
+        bool hasTrafficLights;   
         MapVector2 mean;
 
         char mapName[32];
diff --git a/src/json_serialization.hpp b/src/json_serialization.hpp
index 514acf7e5..7da08fbb5 100644
--- a/src/json_serialization.hpp
+++ b/src/json_serialization.hpp
@@ -24,6 +24,73 @@ namespace madrona_gpudrive
         return 0.0f; // Default z value if not provided
     }
 
+    void from_json(const nlohmann::json &j, TrafficLightState &tl_state)
+    {
+        // Set number of states to the size of the state array
+        size_t numStates = std::max(j.at("state").size(), static_cast<size_t>(consts::kTrajectoryLength));
+        tl_state.numStates = numStates;
+        static const std::unordered_map<std::string, TLState> state_map = {
+                    {"unknown", TLState::Unknown},
+                    {"stop", TLState::Stop},
+                    {"caution", TLState::Caution},
+                    {"go", TLState::Go}
+        };
+
+        // Process each timestep
+        for (size_t t = 0; t < numStates; t++) {
+            // Get the state string and convert to enum
+            if (t < j.at("state").size()) {
+                std::string state_str = j.at("state")[t];
+                auto it = state_map.find(state_str);
+                TLState enum_state = (it != state_map.end()) ? it->second : TLState::Unknown;  // ADD THIS LINE
+                tl_state.state[t] = static_cast<float>(enum_state);  // Cast enum to float
+            } else {
+                tl_state.state[t] = static_cast<float>(TLState::Unknown);  // Cast enum to float
+            }
+
+            // Get the x,y,z positions in a more interpretable fashion
+            if(t < j.at("x").size())
+            {
+                tl_state.x[t] = j.at("x")[t].get<float>();
+            }
+            else
+            {
+                tl_state.x[t] = -1000.0f;
+            }
+            if(t < j.at("y").size())
+            {
+                tl_state.y[t] = j.at("y")[t].get<float>();
+            }
+            else
+            {
+                tl_state.y[t] = -1000.0f;
+            }
+            if(t < j.at("z").size())
+            {
+                tl_state.z[t] = j.at("z")[t].get<float>();
+            }
+            else
+            {
+                tl_state.z[t] = -1000.0f;
+            }
+
+            // Get time index and lane id
+            if (t < j.at("time_index").size()) {
+                tl_state.timeIndex[t] = j.at("time_index")[t];
+            } else {
+                tl_state.timeIndex[t] = -1;
+            }
+        }
+
+        tl_state.laneId = static_cast<int32_t>(j.at("lane_id")[0]);
+
+        // Fill any remaining timesteps with default values
+        for (size_t t = numStates; t < consts::kTrajectoryLength; t++) {
+            tl_state.state[t] = static_cast<float>(TLState::Unknown);
+            tl_state.timeIndex[t] = -1;
+        }
+    }
+
     void from_json(const nlohmann::json &j, MapObject &obj)
     {
         obj.mean = {0,0};
@@ -543,5 +610,23 @@ namespace madrona_gpudrive
             ++idx;
         }
         map.numRoadSegments = countRoadPoints;
+
+        // Process traffic light states if present
+        if (j.contains("tl_states")) {
+            const auto& tl_states = j.at("tl_states");
+            map.numTrafficLights = std::max(tl_states.size(), static_cast<size_t>(consts::kMaxTrafficLightCount));
+            map.hasTrafficLights = (map.numTrafficLights > 0);
+
+            size_t idx = 0;
+            for (auto &kv : tl_states.items())
+            {
+                if (idx >= map.numTrafficLights)
+                    break;
+                kv.value().get_to(map.trafficLightStates[idx++]);
+            }
+        } else {
+            map.numTrafficLights = 0;
+            map.hasTrafficLights = false;
+        }
     }
 }
diff --git a/src/level_gen.cpp b/src/level_gen.cpp
index 06da6cb52..ad3262984 100755
--- a/src/level_gen.cpp
+++ b/src/level_gen.cpp
@@ -495,6 +495,20 @@ void createPersistentEntities(Engine &ctx) {
             other_agents.e[out_idx++] = other_agent;
         }
     }
+
+
+    for(CountT i = 0; i < map.numTrafficLights; i++)
+    {
+        auto &trafficLight = ctx.singleton<TrafficLights>().trafficLights[i];
+        trafficLight = map.trafficLightStates[i];
+        
+        for (size_t t = 0; t < consts::kTrajectoryLength-1; t++) {
+            trafficLight.x[t] -= ctx.singleton<WorldMeans>().mean.x;
+            trafficLight.y[t] -= ctx.singleton<WorldMeans>().mean.y;
+            trafficLight.z[t] -= ctx.singleton<WorldMeans>().mean.z;
+        }
+    }
+
 }
 
 static void resetPersistentEntities(Engine &ctx)
diff --git a/src/mgr.cpp b/src/mgr.cpp
index 55227b95a..cb31bd259 100755
--- a/src/mgr.cpp
+++ b/src/mgr.cpp
@@ -901,6 +901,19 @@ Tensor Manager::metadataTensor() const {
     );
 }
 
+Tensor Manager::trafficLightTensor() const
+{
+    return impl_->exportTensor(
+        ExportID::TrafficLights, 
+        TensorElementType::Float32,
+        {
+            impl_->numWorlds, 
+            consts::kMaxTrafficLightCount, 
+            TrafficLightsStateExportSize
+        }
+    );
+}
+
 Tensor Manager::vbdTrajectoryTensor() const {
     return impl_->exportTensor(
         ExportID::VBDTrajectory, TensorElementType::Float32,
diff --git a/src/mgr.hpp b/src/mgr.hpp
index 9a4ad5fa1..9ae71d4aa 100755
--- a/src/mgr.hpp
+++ b/src/mgr.hpp
@@ -70,6 +70,7 @@ class Manager {
     MGR_EXPORT madrona::py::Tensor expertTrajectoryTensor() const;
     MGR_EXPORT madrona::py::Tensor worldMeansTensor() const;
     MGR_EXPORT madrona::py::Tensor metadataTensor() const;
+    MGR_EXPORT madrona::py::Tensor trafficLightTensor() const;
     MGR_EXPORT madrona::py::Tensor deletedAgentsTensor() const;
     MGR_EXPORT madrona::py::Tensor mapNameTensor() const;
     MGR_EXPORT madrona::py::Tensor scenarioIdTensor() const;
diff --git a/src/sim.cpp b/src/sim.cpp
index b6d460336..7cdddcea1 100755
--- a/src/sim.cpp
+++ b/src/sim.cpp
@@ -60,7 +60,7 @@ void Sim::registerTypes(ECSRegistry &registry, const Config &cfg)
     registry.registerComponent<MapType>();
     registry.registerComponent<MetaData>();
     registry.registerComponent<VBDTrajectory>();
-
+    registry.registerSingleton<TrafficLights>();
     registry.registerSingleton<WorldReset>();
     registry.registerSingleton<Shape>();
     registry.registerSingleton<Map>();
@@ -84,6 +84,7 @@ void Sim::registerTypes(ECSRegistry &registry, const Config &cfg)
     registry.exportSingleton<DeletedAgents>((uint32_t)ExportID::DeletedAgents);
     registry.exportSingleton<MapName>((uint32_t)ExportID::MapName);
     registry.exportSingleton<ScenarioId>((uint32_t)ExportID::ScenarioId);
+    registry.exportSingleton<TrafficLights>((uint32_t)ExportID::TrafficLights);
 
     registry.exportColumn<AgentInterface, Action>(
         (uint32_t)ExportID::Action);
@@ -93,7 +94,6 @@ void Sim::registerTypes(ECSRegistry &registry, const Config &cfg)
         (uint32_t)ExportID::AgentMapObservations);
     registry.exportColumn<RoadInterface, MapObservation>(
         (uint32_t)ExportID::MapObservation);
-
     registry.exportColumn<AgentInterface, PartnerObservations>(
         (uint32_t)ExportID::PartnerObservations);
     registry.exportColumn<AgentInterface, Lidar>(
@@ -1155,7 +1155,7 @@ void setupRestOfTasks(TaskGraphBuilder &builder, const Sim::Config &cfg,
     //         Progress,
     //         OtherAgents,
     //         EntityType,
-    //         CollisionDetectionEvent,
+    //         CollisionDetectionEvent,f
     //         AgentInterfaceEntity
     //     >>({clear_tmp});
 
diff --git a/src/sim.hpp b/src/sim.hpp
index bc8ad1f4d..b1084995f 100755
--- a/src/sim.hpp
+++ b/src/sim.hpp
@@ -42,7 +42,8 @@ enum class ExportID : uint32_t {
     MapName,
     ScenarioId,
     VBDTrajectory,
-    NumExports
+    TrafficLights,
+    NumExports // NumExports should always be the last entry in this enum
 };
 
 // Stores values for the ObjectID component that links entities to
@@ -81,26 +82,26 @@ struct Sim : public madrona::WorldBase {
                               const Config &cfg);
 
     // Sim::setupTasks is called during initialization to build
-    // the system task graph that will be invoked by the 
+    // the system task graph that will be invoked by the
     // Manager class (src/mgr.hpp) for each step.
     static void setupTasks(madrona::TaskGraphManager &taskgraph_mgr,
                            const Config &cfg);
 
-   const std::pair<EntityType,EntityType> collisionPairs[20] = {                                                                                                 
-                                                              {EntityType::Pedestrian, EntityType::RoadEdge},                                                                                                          
-                                                              {EntityType::Pedestrian, EntityType::RoadLine},                                                                                                                
-                                                              {EntityType::Pedestrian, EntityType::RoadLane},                                                                                                                
-                                                              {EntityType::Pedestrian, EntityType::CrossWalk},                                                                                                               
-                                                              {EntityType::Pedestrian, EntityType::SpeedBump},                                                                                                                
-                                                              {EntityType::Cyclist, EntityType::RoadEdge},                                                                                                                   
-                                                              {EntityType::Cyclist, EntityType::RoadLine},                                                                                                                   
-                                                              {EntityType::Cyclist, EntityType::RoadLane},                                                                                                                   
-                                                              {EntityType::Cyclist, EntityType::CrossWalk},                                                                                                                  
-                                                              {EntityType::Cyclist, EntityType::SpeedBump},                                                                                                                 
-                                                              {EntityType::Vehicle, EntityType::CrossWalk},                                                                                                                  
-                                                              {EntityType::Vehicle, EntityType::SpeedBump},                                                                                                                  
-                                                              {EntityType::Vehicle, EntityType::RoadLine},                                                                                                                   
-                                                              {EntityType::Vehicle, EntityType::RoadLane}};                   
+   const std::pair<EntityType,EntityType> collisionPairs[20] = {
+                                                              {EntityType::Pedestrian, EntityType::RoadEdge},
+                                                              {EntityType::Pedestrian, EntityType::RoadLine},
+                                                              {EntityType::Pedestrian, EntityType::RoadLane},
+                                                              {EntityType::Pedestrian, EntityType::CrossWalk},
+                                                              {EntityType::Pedestrian, EntityType::SpeedBump},
+                                                              {EntityType::Cyclist, EntityType::RoadEdge},
+                                                              {EntityType::Cyclist, EntityType::RoadLine},
+                                                              {EntityType::Cyclist, EntityType::RoadLane},
+                                                              {EntityType::Cyclist, EntityType::CrossWalk},
+                                                              {EntityType::Cyclist, EntityType::SpeedBump},
+                                                              {EntityType::Vehicle, EntityType::CrossWalk},
+                                                              {EntityType::Vehicle, EntityType::SpeedBump},
+                                                              {EntityType::Vehicle, EntityType::RoadLine},
+                                                              {EntityType::Vehicle, EntityType::RoadLane}};
 
     // The constructor is called for each world during initialization.
     // Config is global across all worlds, while WorldInit (src/init.hpp)
diff --git a/src/types.hpp b/src/types.hpp
index c4823d66d..f18bf9683 100755
--- a/src/types.hpp
+++ b/src/types.hpp
@@ -437,6 +437,41 @@ namespace madrona_gpudrive
     const size_t ScenarioIdExportSize = 32;
     static_assert(sizeof(ScenarioId) == sizeof(char32_t) * ScenarioIdExportSize);
 
+    enum class TLState : int32_t
+    {
+        Unknown = 0,
+        Stop = 1,
+        Caution = 2,
+        Go = 3
+    };
+
+    struct TrafficLightState
+    {
+        // Lane ID for this traffic light
+        float laneId = 0;
+
+        // Arrays of state data for each timestep
+        float state[consts::kTrajectoryLength] = {};
+        float x [consts::kTrajectoryLength] = {};
+        float y [consts::kTrajectoryLength] = {};
+        float z [consts::kTrajectoryLength] = {};
+        float timeIndex[consts::kTrajectoryLength] = {};
+        // Number of valid states
+        float numStates = 0;
+    };
+
+    // 1 (lane_id) + 5 (state, x, y, z, timeIndex) + 1 (numStates) = 6
+    const size_t TrafficLightsStateExportSize = 1 + (consts::kTrajectoryLength) * 5 + 1;
+    static_assert(sizeof(TrafficLightState) == sizeof(float) * TrafficLightsStateExportSize);
+
+    struct TrafficLights
+    {
+        TrafficLightState trafficLights[consts::kMaxTrafficLightCount];
+    };
+
+    const size_t TrafficLightsExportSize = consts::kMaxTrafficLightCount * TrafficLightsStateExportSize;
+    static_assert(sizeof(TrafficLights) == sizeof(float) * TrafficLightsExportSize);
+
     struct MetaData
     {
         float isSdc;
@@ -475,9 +510,7 @@ namespace madrona_gpudrive
                                 AgentID,
                                 MetaData,
                                 VBDTrajectory,
-
                                 ControlledState // Drive Logic
-
                                 >
     {
     };

From fbf09100bb59bd0b51dba5247ffdaacb6612fd7e Mon Sep 17 00:00:00 2001
From: Ellington Kirby <ellingtonkirby@gmail.com>
Date: Fri, 20 Jun 2025 14:50:36 +0000
Subject: [PATCH 38/38] [FIX] adapt TL+other missing changes to new json
 parsing

---
 src/json_serialization.hpp | 130 ++++++++++++++++++++++++++++++++++++-
 1 file changed, 128 insertions(+), 2 deletions(-)

diff --git a/src/json_serialization.hpp b/src/json_serialization.hpp
index afe787cab..6d53066a2 100644
--- a/src/json_serialization.hpp
+++ b/src/json_serialization.hpp
@@ -131,6 +131,80 @@ namespace madrona_gpudrive
         }
     }
 
+    void from_dom(const simdjson::dom::element &j, TrafficLightState &tl_state)
+    {
+        // Set number of states to the size of the state array
+        simdjson::dom::array states = getValueOrDefault<simdjson::dom::array>(j, "state", {});
+        simdjson::dom::array x = getValueOrDefault<simdjson::dom::array>(j, "x", {});
+        simdjson::dom::array y = getValueOrDefault<simdjson::dom::array>(j, "y", {});
+        simdjson::dom::array z = getValueOrDefault<simdjson::dom::array>(j, "z", {});
+        simdjson::dom::array timeIndex = getValueOrDefault<simdjson::dom::array>(j, "time_index", {});
+        simdjson::dom::array laneId = getValueOrDefault<simdjson::dom::array>(j, "lane_id", {});
+        size_t numStates = std::max(states.size(), static_cast<size_t>(consts::kTrajectoryLength));
+        tl_state.numStates = numStates;
+        static const std::unordered_map<std::string_view, TLState> state_map = {
+                    {"unknown", TLState::Unknown},
+                    {"stop", TLState::Stop},
+                    {"caution", TLState::Caution},
+                    {"go", TLState::Go}
+        };
+
+        // Process each timestep
+        for (size_t t = 0; t < numStates; t++) {
+            // Get the state string and convert to enum
+            if (t < states.size()) {
+                std::string_view state_str = getValueOrDefault<std::string_view>(states, t, "unknown");
+                auto it = state_map.find(state_str);
+                TLState enum_state = (it != state_map.end()) ? it->second : TLState::Unknown;  // ADD THIS LINE
+                tl_state.state[t] = static_cast<float>(enum_state);  // Cast enum to float
+            } else {
+                tl_state.state[t] = static_cast<float>(TLState::Unknown);  // Cast enum to float
+            }
+
+            // Get the x,y,z positions in a more interpretable fashion
+            if(x.size() > 0)
+            {
+                tl_state.x[t] = getValueOrDefault<float>(x, 0, 0.0f);
+            }
+            else
+            {
+                tl_state.x[t] = -1000.0f;
+            }
+            if(y.size() > 0)
+            {
+                tl_state.y[t] = getValueOrDefault<float>(y, 0, 0.0f);
+            }
+            else
+            {
+                tl_state.y[t] = -1000.0f;
+            }
+            if(z.size() > 0)
+            {
+                tl_state.z[t] = getValueOrDefault<float>(z, 0, 0.0f);
+            }
+            else
+            {
+                tl_state.z[t] = -1000.0f;
+            }
+
+            // Get time index and lane id
+            if (t < timeIndex.size()) {
+                tl_state.timeIndex[t] = getValueOrDefault<float>(timeIndex, t, 0.0f);
+            } else {
+                tl_state.timeIndex[t] = -1;
+            }
+        }
+
+        if (laneId.size() > 0) {
+            tl_state.laneId = getValueOrDefault<int>(laneId, 0, 0);
+        }
+        // Fill any remaining timesteps with default values
+        for (size_t t = numStates; t < consts::kTrajectoryLength; t++) {
+            tl_state.state[t] = static_cast<float>(TLState::Unknown);
+            tl_state.timeIndex[t] = -1;
+        }
+    }
+
     void from_dom(const simdjson::dom::element &j, MapVector2 &p) {
         p.x = getValueOrDefault<float>(j, "x", 0.0f);  // Provide a default value of 0.0f if missing
         p.y = getValueOrDefault<float>(j, "y", 0.0f);
@@ -141,13 +215,21 @@ namespace madrona_gpudrive
         obj.mean = {0,0};
         uint32_t i = 0;
         simdjson::dom::array positions = getValueOrDefault<simdjson::dom::array>(j, "position", {});
-
+        float zPositions[10];
         for (const auto &pos : positions)
         { 
             if (i >= MAX_POSITIONS) break; // Avoid overflow
             from_dom(pos, obj.position[i]);
             obj.mean.x += (obj.position[i].x - obj.mean.x)/(i+1);
             obj.mean.y += (obj.position[i].y - obj.mean.y)/(i+1);
+
+            // Store z position separately
+            float z = getValueOrDefault<float>(pos, "z", 0.0f);
+            if (i < 10)
+            {
+                zPositions[i] = z;
+            }
+
             ++i;
         }
         obj.numPositions = i;
@@ -218,6 +300,26 @@ namespace madrona_gpudrive
         obj.metadata.isTrackToPredict = 0;
         obj.metadata.difficulty = 0;
 
+        // Calculate average z position from the first 10 positions
+        // Store in the avgZ field of map object
+        float avgZ = 0.0f;
+        float avg_count = 0.0f;
+        for (int zi = 0; zi < 10 && zi < obj.numValid; zi++) {
+            // Only consider valid z positions
+            if (zi < obj.numValid && obj.valid[zi]) {
+                avgZ += zPositions[zi];
+                avg_count += 1;
+            }
+        }
+        if (avg_count == 0) {
+            avgZ = 0.0f; // Avoid division by zero
+        }
+        else
+        {
+            avgZ /= avg_count; // Calculate average
+        }
+        obj.metadata.avgZ = avgZ;
+
         // Initialize VBD trajectories to zeros
         for (int i = 0; i < consts::kTrajectoryLength; i++) {
             for (int j = 0; j < 6; j++) {
@@ -300,6 +402,12 @@ namespace madrona_gpudrive
         const int64_t num_segments = geometry.size() - 1;
         const int64_t sample_every_n_ = 1;
         const int64_t num_sampled_points = (num_segments + sample_every_n_ - 1) / sample_every_n_ + 1;
+
+        const int64_t geometry_size = static_cast<int64_t>(geometry_points_.size());
+        if (geometry_size == 0) {
+            road.numPoints = 0;
+            return;
+        }
         
         if (num_segments >= 10 && (road.type == EntityType::RoadLane || road.type == EntityType::RoadEdge || road.type == EntityType::RoadLine))
         {
@@ -461,7 +569,6 @@ namespace madrona_gpudrive
         
         auto mean = calc_mean(j);
         map.mean = {mean.first, mean.second};
-        // map.numObjects = std::min(getValueOrDefault<size_t>(j, "objects", 0), static_cast<size_t>(MAX_OBJECTS));
         auto objects = getValueOrDefault<simdjson::dom::array>(j, "objects", {});
         map.numObjects = std::min(objects.size(), static_cast<size_t>(MAX_OBJECTS));
 
@@ -593,5 +700,24 @@ namespace madrona_gpudrive
             ++idx;
         }
         map.numRoadSegments = countRoadPoints;
+
+        map.numTrafficLights = 0;
+        map.hasTrafficLights = false;
+        auto tl_states_element = getValueOrDefault<simdjson::dom::element>(j, "tl_states", simdjson::dom::element());
+        simdjson::dom::object tl_states;
+        if (tl_states_element.is_object() && tl_states_element.get_object().get(tl_states) == simdjson::SUCCESS) {
+            if (std::begin(tl_states) != std::end(tl_states)) {
+                // non-empty object
+                map.numTrafficLights = std::max(tl_states.size(), static_cast<size_t>(consts::kMaxTrafficLightCount));
+                map.hasTrafficLights = (map.numTrafficLights > 0);
+                for (auto [key, value] : tl_states) {
+                    size_t idx = 0;
+                    if (idx >= map.numTrafficLights)
+                        break;
+                    from_dom(value, map.trafficLightStates[idx]);
+                    ++idx;
+                }
+            }
+        }
     }
 }
\ No newline at end of file