init

no more .mpt

Merge remote-tracking branch 'origin/main' into richard-unifympt

slim

policy spec handler

more concise

cleanup

simplify

Merge remote-tracking branch 'origin/main' into richard-unifympt

re-add

fix policy spex

Update packages/mettagrid/python/src/mettagrid/util/uri_resolvers/schemes.py

Co-authored-by: graphite-app[bot] <96075541+graphite-app[bot]@users.noreply.github.com>

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bundles

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bundle

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Merge remote-tracking branch 'origin/main' into richard-unifympt

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Merge remote-tracking branch 'origin/main' into richard-unifympt

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simplify?

ugh compat

cleanup

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cleanup

tests

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more tests

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simplify?

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cleanup

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Merge remote-tracking branch 'origin/main' into richard-unifympt

Merge remote-tracking branch 'origin/main' into richard-unifympt

no more .mpt

remove all .mpt and lint

cleanup

local data path fixes

mpt re-add

re-add artifact

lint

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more cleanup

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diff cleanup

ftt

lint

fix error

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more tests

lint

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checkpoint policy does save/load

lint

checkpoint moving

catcus

lint

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fold-in

[pyright 4] Get pyright to pass on app_backend (#4478)

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Fix command, add space (#4456)

added space to --app:lib--tlsEmulation:off which makes it --app:lib
--tlsEmulation:off
now it runs

Rename HyperUpdateRule to ScheduleRule (#4483)

- rename HyperUpdateRule to ScheduleRule and apply to TrainerConfig via
target_path
- update recipes and teacher scheduling to use ScheduleRule
- report PPO stats using ppo_actor/ppo_critic hyperparam keys and update
tests

- not run (not requested)

---------

Co-authored-by: graphite-app[bot] <96075541+graphite-app[bot]@users.noreply.github.com>

Merge remote-tracking branch 'origin/main' into richard-unifympt

Fix supervisor teacher behavior and legacy BC mode (#4484)

- gate PPO actor during supervisor teacher phase
- fix supervisor/no-teacher behavior and add legacy BC (no gating, no
PPO resume)
- require supervisor policy URI for sliced_cloner_no_ppo

- not run (not requested)

---------

Co-authored-by: graphite-app[bot] <96075541+graphite-app[bot]@users.noreply.github.com>
Co-authored-by: Adam S <134907338+gustofied@users.noreply.github.com>

Minor fixes to the slstm triton kernel, causing failures for certain kernel sizes (#4492)

cleanup

Merge remote-tracking branch 'origin/main' into richard-unifympt

fold in

training environments and eval environments mismatched (#4487)

I ran a direct config comparison using the training entrypoint
(recipes/experiment/cogs_v_clips.train) with variants=["heart_chorus"]
and compared the eval suite
config it builds (difficulty standard + heart_chorus) for an overlapping
mission: hello_world.oxygen_bottleneck.

  Findings:

  - Compass is ON in both training and eval (global_obs.compass=True).
- Vibe count and change‑vibe settings match (152 vibes;
change_vibe.number_of_vibes=152).
- But the mission parameters differ between training and eval for the
same mission name:
      - game.objects.carbon_extractor.max_uses: train 25 vs eval 100
      - game.objects.oxygen_extractor.max_uses: train 5 vs eval 20
      - game.objects.germanium_extractor.max_uses: train 5 vs eval 20
      - game.objects.silicon_extractor.max_uses: train 120 vs eval 480

So the mismatch isn’t compass — it’s the mission definitions used by
training vs eval. Training uses base missions
(cogames.cogs_vs_clips.missions), while eval uses
integrated eval missions (cogames.cogs_vs_clips.evals.integrated_evals)
that have different extractor settings.

Also: the eval suite used by recipes/experiment/cogs_v_clips.train does
not include machina_1.open_world at all (it only evaluates the 7
HELLO_WORLD integrated
evals). So training can be creating hearts on easier missions while your
eval runs on machina_1 are a different environment entirely.

  Relevant files:

- Compass default: packages/cogames/src/cogames/cogs_vs_clips/mission.py
- Training entrypoint + eval suite wiring:
recipes/experiment/cogs_v_clips.py
- Eval mission definitions:
packages/cogames/src/cogames/cogs_vs_clips/evals/integrated_evals.py

If you want true parity, we should align which mission templates eval
uses (and/or include machina_1.open_world in the eval suite). I can
patch this if you want —
  tell me whether you prefer:

1. Eval suite uses the same mission templates as training (from
missions.py), or
  2. Training uses the integrated eval mission definitions, or
  3. Add machina_1.open_world to the eval suite.

ripping out

Merge remote-tracking branch 'origin/main' into richard-unifympt

simplify

fix and lint

choke

simplify submission zip creation

use policy_spec for submission zips

tighten checkpoint io helpers

shorten checkpoint arg help

inline checkpoint policy helpers

restore policy spec docstring

validate checkpoint data_path before download

require checkpoint directory URIs

expand policy spec s3 docstring

Author: relh

metta/rl/loss/sl_checkpointed_kickstarter.py

@@ -148,6 +148,8 @@ def _construct_checkpoint_uri(self, epoch: int) -> str:
         filename = checkpoint_filename(run_name, epoch)
 
         if parsed.scheme == "file" and parsed.local_path:
+            if parsed.local_path.is_file():
+                raise ValueError("Provide a checkpoint directory, not policy_spec.json")
             path = parsed.local_path.parent / filename
             return f"file://{path}"
         elif parsed.scheme == "s3" and parsed.bucket and parsed.key:

metta/rl/metta_scheme_resolver.py

@@ -7,7 +7,6 @@
 from metta.app_backend.metta_repo import PolicyVersionWithName
 from metta.common.util.constants import PROD_STATS_SERVER_URI
 from mettagrid.util.uri_resolvers.base import MettaParsedScheme, SchemeResolver
-from mettagrid.util.uri_resolvers.schemes import resolve_uri
 
 logger = logging.getLogger(__name__)
 
@@ -118,12 +117,6 @@ def get_path_to_policy_spec_or_mpt(self, uri: str) -> str:
             logger.info(f"Metta scheme resolver: {uri} resolved to s3 policy spec: {policy_version.s3_path}")
             return policy_version.s3_path
 
-        # If that is missing (probably legacy policy), we send you to the mpt file, and will later assume
-        # that the class to hydrate from is MptPolicy
-        mpt_file_path = (policy_version.policy_spec or {}).get("init_kwargs", {}).get("checkpoint_uri")
-        if not mpt_file_path:
-            raise ValueError(f"Data not found for policy version {policy_version.id}")
-        if not mpt_file_path.endswith(".mpt"):
-            raise ValueError(f"Invalid mpt file path: {mpt_file_path}")
-        logger.info(f"Metta scheme resolver: {uri} resolved to mpt checkpoint: {mpt_file_path}")
-        return resolve_uri(mpt_file_path).canonical
+        raise ValueError(
+            f"Policy version {policy_version.id} has no s3_path; expected a policy spec submission zip in S3."
+        )

packages/cogames/scripts/run_evaluation.py

@@ -33,6 +33,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import torch
+from safetensors.torch import load as load_safetensors
 
 from cogames.cogs_vs_clips.evals.diagnostic_evals import DIAGNOSTIC_EVALS
 from cogames.cogs_vs_clips.mission import Mission, MissionVariant, NumCogsVariant
@@ -85,16 +86,20 @@ def _get_policy_action_space(policy_path: str) -> Optional[int]:
             return None
 
     try:
-        if policy_path.endswith(".mpt"):
-            from mettagrid.policy.mpt_artifact import load_mpt
+        spec = policy_spec_from_uri(policy_path)
+        if not spec.data_path:
+            return None
+        weights = load_safetensors(Path(spec.data_path).read_bytes())
 
-            artifact = load_mpt(policy_path)
-            action_space = _action_space_from_state_dict(artifact.state_dict)
-        else:
-            from mettagrid.policy.checkpoint_policy import load_state_from_checkpoint_uri
+        # Look for actor head weight to determine action space
+        for key, tensor in weights.items():
+            if "actor_head" in key and "weight" in key and len(tensor.shape) == 2:
+                action_space = tensor.shape[0]
+                _policy_action_space_cache[policy_path] = action_space
+                logger.info(f"Detected policy action space: {action_space} actions")
+                return action_space
 
-            _, state_dict = load_state_from_checkpoint_uri(policy_path, device="cpu")
-            action_space = _action_space_from_state_dict(state_dict)
+        return None
     except Exception as e:
         logger.warning(f"Failed to detect policy action space: {e}")
         return None

packages/mettagrid/python/src/mettagrid/util/uri_resolvers/README.md

@@ -2,28 +2,30 @@
 
 This package provides a pluggable URI resolution system for handling different resource schemes.
 
+Checkpoint URIs point at a checkpoint directory containing `policy_spec.json`.
+
 ## Usage
 
 ```python
 from mettagrid.util.uri_resolvers.schemes import parse_uri, resolve_uri, get_checkpoint_metadata
 
 # Parse a URI to get its components
-parsed = parse_uri("s3://bucket/path/to/file.mpt")
+parsed = parse_uri("s3://bucket/path/to/run:v5")
 print(parsed.scheme)  # "s3"
 print(parsed.bucket)  # "bucket"
-print(parsed.key)     # "path/to/file.mpt"
+print(parsed.key)     # "path/to/run:v5"
 
 # Get checkpoint info (run_name, epoch) from parsed URI
 info = parsed.checkpoint_info  # ("run_name", 5) or None
 if info:
     run_name, epoch = info
 
 # Resolve a URI (normalizes and finds latest checkpoint if applicable)
-parsed = resolve_uri("file:///path/to/checkpoints")
-print(parsed.canonical)  # "file:///path/to/checkpoints/run:v5.mpt"
+parsed = resolve_uri("file:///path/to/checkpoints:latest")
+print(parsed.canonical)  # "file:///path/to/checkpoints/run:v5"
 
 # Get full checkpoint metadata (resolves URI first)
-metadata = get_checkpoint_metadata("s3://bucket/checkpoints/my-run:v5.mpt")
+metadata = get_checkpoint_metadata("s3://bucket/checkpoints/my-run:v5")
 print(metadata.run_name)  # "my-run"
 print(metadata.epoch)     # 5
 print(metadata.uri)       # resolved URI