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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.