Here are a few multi-agent games that can be learned using RL.

• To run pursuit, use the command python3 main_run.py pursuit.
• To run waterworld, use the command python3 main_run.py waterworld.
• To run multiwalker, use the command python3 main_run.py multiwalker.

This repo uses the Reinforcement Library toolkit RLlib from Ray. The specific wheels Ray package that are needed are included in this repo (there have been breaking changes). Once the wheel is downloaded, install it as pip install -U path/to/wheel.whl. Install the other required packages using the command pip install -r requirements.txt. Required Python version is 3.7.6.

• Run python3 parameterSharingPursuit.py RLmethod to train an RL method RLmethod (e.g. PPO) on pursuit.
• Run python3 parameterSharingMultiwalker.py RLmethod to train an RL method RLmethod (e.g. PPO) on multiwalker.
• Run python3 parameterSharingWaterworld.py RLmethod to train an RL method RLmethod (e.g. PPO) on waterworld.

Use play_pursuit.py, play_waterworld.py and play_multiwalker.py to re-play the games using the learned RL policies. Simply change the checkpoint_path variable and make sure params.pkl is in the correct place, relative to the former.

• RLlib has many interesting and unresolved bugs regarding logging episode reward vs policy reward, and them sometimes being off by a factor of the number of agents. This is why the data is sometimes scaled in the files which generate plots. We verified we scaled them correctly by playing the trained policies and adding up typical rewards ourselves in each case.