Recent advances in deep reinforcement learning (RL) have led to considerable progress in many 2-player zero-sum games, such as Go, Poker and Starcraft. The purely adversarial nature of such games allows for conceptually simple and principled application of RL methods. However real-world settings are many-agent, and agent interactions are complex mixtures of common-interest and competitive aspects. We consider Diplomacy, a 7-player board game designed to accentuate dilemmas resulting from many-agent interactions. It also features a large combinatorial action space and simultaneous moves, which are challenging for RL algorithms. We propose a simple yet effective approximate best response operator, designed to handle large combinatorial action spaces and simultaneous moves. We also introduce a family of policy iteration methods that approximate fictitious play. With these methods, we successfully apply RL to Diplomacy: we show that our agents convincingly outperform the previous state-of-the-art, and game theoretic equilibrium analysis shows that the new process yields consistent improvements.


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    Title :

    Learning to Play No-Press Diplomacy with Best Response Policy Iteration


    Contributors:
    Anthony, TW (author) / Eccles, T (author) / Tacchetti, A (author) / Kramár, J (author) / Gemp, IM (author) / Hudson, TC (author) / Porcel, N (author) / Lanctot, M (author) / Pérolat, J (author) / Everett, R (author)

    Publication date :

    2020-12-12


    Remarks:

    In: Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020). NeurIPS (2020) (In press).


    Type of media :

    Paper


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    DDC:    629



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