A Multi Agent RL agent that solves Unity's Tennis environment for a collaboration task

Overview: Broad goal is to train an agent to successfully solve Unity's Tennis environment.

Goal & Rewards: 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.

State Space: The state space consists of 8 variables corresponding to the position and velocity of the ball and racket. Each agent receives its own, local observation.

Action Space: Actions are continuous. Each action is a vector with 2 numbers, corresponding to moves toward (or away from) the net, and jumping.

Solving condition: The task is episodic, and in order to solve the environment, agents must get an average score of +0.5 (over 100 consecutive episodes, after taking the maximum over both agents). Specifically,

• After each episode, we add up the rewards that each agent received (without discounting), to get a score for each agent. This yields 2 (potentially different) scores. We then take the maximum of these 2 scores.
• This yields a single score for each episode.

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

1. Create (and activate) a new environment with Python 3.6.

• Linux or Mac:

  conda create --name collaboration_competition python=3.6 source activate collaboration_competition

• Windows:

  conda create --name collaboration_competition python=3.6

  activate collaboration_competition

2. Clone the repository, Then, install several dependencies.

   git clone https://github.com/ramanshrivastava/multi-agent-rl-tennis.git

   pip install -r requirements.txt

3. Create an IPython kernel for the collaboration_competition environment.

   python -m ipykernel install --user --name collaboration_competition --display-name "collaboration_competition"

4. Before running code in a notebook, change the kernel to match the collaboration_competition environment by using the drop-down Kernel menu.

1. Download the environment from one of the links below. You need only select the environment that matches your operating system:

• Linux: click here
• Mac OSX: click here
• Windows (32-bit): click here
• Windows (64-bit): click here

(For Windows users) Check out this link 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), then please use this link to obtain the environment.

2. Place the file in the multi-agent-rl-tennis GitHub repository, in the root folder, and unzip (or decompress) the file.

1. Open the Tennis.ipynb jupyter notebook and execute each code cell to train the agent.
2. Once the necessary packages are imported as a next step provide the file_name for the UnityEnvironment
3. Execute the rest of the cells as it is there after
4. provide the PATH for .pth files in the code cell 9 to save actor and critic model weights.