Deep Counterfactual Regret Minimization in a Multi-AgentIncomplete Information Game of “Take 5”

  • Take-5 is a multi-agent repeating imperfect-information normal-form tabletop card game. Players aim to anticipate their opponents’ moves and strategize with the cards in their hand to optimize their outcomes and maximize utility.
  • Based on a paper “Deep Counterfactual Regret Minimization” by N. Brown et al. I adapted it to allow for the agent to accurately predict counterfactual regret vectors for this game.
  • With lack of better assessment, the agent was able to beat my group of friends who play the game very often.

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