Comparing Exploitation-Based and Game Theory Optimal Based Approaches in a Multi-Agent Environment

In this project, we compared two algorithms

• BPR: exploitative style, a way of playing to identify and exploit imbalances in the strategies of your opponents.
• MADDPG/M3DDPG: game theory optimal (GTO) style, a way of playing a game that makes you unexploitable to your opponents.

Check the report for more detail.

Remarks

• env.py is the environment we developed to test the algorithms. You can interact with the environment by running play_with_model.py.
• train/ folder contains the code we used to train our agent.
  • Notice that you may need to add sys.path.append to make import env works
• For the MADDPG/M3DDPG agents, we stored them as pickle objects after training for reuse.