Skip to content

Bump jinja2 from 2.10 to 2.11.3 #13

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
dependabot wants to merge 2 commits into
base: master
Choose a base branch
from
Open

Bump jinja2 from 2.10 to 2.11.3 #13

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
0ee2057
Update README.md
elifons Feb 13, 2019
556968e
Bump jinja2 from 2.10 to 2.11.3
dependabot[bot] Mar 20, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
47 changes: 18 additions & 29 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,11 @@ For this project, you will work with the [Tennis](https://github.com/Unity-Techn

![Trained Agent][image1]

In this environment, two agents control rackets to bounce a ball over a net. If an agent hits the ball over the net, it receives a reward of +0.1. If an agent lets a ball hit the ground or hits the ball out of bounds, it receives a reward of -0.01. Thus, the goal of each agent is to keep the ball in play.
In this environment, two agents control rackets to bounce a ball over a net.

### Environment

If an agent hits the ball over the net, it receives a reward of +0.1. If an agent lets a ball hit the ground or hits the ball out of bounds, it receives a reward of -0.01. Thus, the goal of each agent is to keep the ball in play.

The observation space consists of 8 variables corresponding to the position and velocity of the ball and racket. Each agent receives its own, local observation. Two continuous actions are available, corresponding to movement toward (or away from) the net, and jumping.

Expand All @@ -23,40 +27,25 @@ The task is episodic, and in order to solve the environment, your agents must ge

The environment is considered solved, when the average (over 100 episodes) of those **scores** is at least +0.5.

### Getting Started
### Installation

1. Clone repository:

* git clone https://github.com/elifons/DeepRL-Multi-agent.git
* cd DeepRL-Multi-agent
* pip install -r requirements.txt

1. Download the environment from one of the links below. You need only select the environment that matches your operating system:
Alternatively, follow the instractions on this link https://github.com/udacity/deep-reinforcement-learning#dependencies to set up a python environment.

2. Download the environment from one of the links below. You need only select the environment that matches your operating system:
- Linux: [click here](https://s3-us-west-1.amazonaws.com/udacity-drlnd/P3/Tennis/Tennis_Linux.zip)
- Mac OSX: [click here](https://s3-us-west-1.amazonaws.com/udacity-drlnd/P3/Tennis/Tennis.app.zip)
- Windows (32-bit): [click here](https://s3-us-west-1.amazonaws.com/udacity-drlnd/P3/Tennis/Tennis_Windows_x86.zip)
- Windows (64-bit): [click here](https://s3-us-west-1.amazonaws.com/udacity-drlnd/P3/Tennis/Tennis_Windows_x86_64.zip)

(_For Windows users_) Check out [this link](https://support.microsoft.com/en-us/help/827218/how-to-determine-whether-a-computer-is-running-a-32-bit-version-or-64) if you need help with determining if your computer is running a 32-bit version or 64-bit version of the Windows operating system.

(_For AWS_) If you'd like to train the agent on AWS (and have not [enabled a virtual screen](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Training-on-Amazon-Web-Service.md)), then please use [this link](https://s3-us-west-1.amazonaws.com/udacity-drlnd/P3/Tennis/Tennis_Linux_NoVis.zip) to obtain the "headless" version of the environment. You will **not** be able to watch the agent without enabling a virtual screen, but you will be able to train the agent. (_To watch the agent, you should follow the instructions to [enable a virtual screen](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Training-on-Amazon-Web-Service.md), and then download the environment for the **Linux** operating system above._)

2. Place the file in the DRLND GitHub repository, in the `p3_collab-compet/` folder, and unzip (or decompress) the file.

### Instructions

Follow the instructions in `Tennis.ipynb` to get started with training your own agent!

### (Optional) Challenge: Crawler Environment

After you have successfully completed the project, you might like to solve the more difficult **Soccer** environment.

![Soccer][image2]

In this environment, the goal is to train a team of agents to play soccer.

You can read more about this environment in the ML-Agents GitHub [here](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Learning-Environment-Examples.md#soccer-twos). To solve this harder task, you'll need to download a new Unity environment. (**Note**: Udacity students should not submit a project with this new environment.)
3. Place the file in the DRLND GitHub repository, in the `p3_collab-compet/` folder, and unzip (or decompress) the file.

You need only select the environment that matches your operating system:
- Linux: [click here](https://s3-us-west-1.amazonaws.com/udacity-drlnd/P3/Soccer/Soccer_Linux.zip)
- Mac OSX: [click here](https://s3-us-west-1.amazonaws.com/udacity-drlnd/P3/Soccer/Soccer.app.zip)
- Windows (32-bit): [click here](https://s3-us-west-1.amazonaws.com/udacity-drlnd/P3/Soccer/Soccer_Windows_x86.zip)
- Windows (64-bit): [click here](https://s3-us-west-1.amazonaws.com/udacity-drlnd/P3/Soccer/Soccer_Windows_x86_64.zip)
### Getting started

Then, place the file in the `p3_collab-compet/` folder in the DRLND GitHub repository, and unzip (or decompress) the file. Next, open `Soccer.ipynb` and follow the instructions to learn how to use the Python API to control the agent.
Follow the instructions in `Continuous_Control.ipynb` to get started with training the agent!

(_For AWS_) If you'd like to train the agents on AWS (and have not [enabled a virtual screen](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Training-on-Amazon-Web-Service.md)), then please use [this link](https://s3-us-west-1.amazonaws.com/udacity-drlnd/P3/Soccer/Soccer_Linux_NoVis.zip) to obtain the "headless" version of the environment. You will **not** be able to watch the agents without enabling a virtual screen, but you will be able to train the agents. (_To watch the agents, you should follow the instructions to [enable a virtual screen](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Training-on-Amazon-Web-Service.md), and then download the environment for the **Linux** operating system above._)
2 changes: 1 addition & 1 deletion requirements.txt
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,7 @@ ipython-genutils==0.2.0
ipywidgets==7.4.2
itsdangerous==1.1.0
jedi==0.13.2
Jinja2==2.10
Jinja2==2.11.3
jsonschema==2.6.0
jupyter==1.0.0
jupyter-client==5.2.4
Expand Down