To achieve general intelligence, agents must learn how to interact with others in a shared environment: this is the challenge of multiagent reinforcement learning (MARL). The simplest form is independent reinforcement learning (InRL), where each agent treats its experience as part of its (non-stationary) environment. In this paper, we first observe that policies learned using InRL can overfit to the other agents’ policies during training, failing to sufficiently generalize duringn execution. We introduce a new metric, joint-policy correlation, to quantify this effect. We describe an algorithm for general MARL, based on approximate best responses to mixtures of policies generated using deep reinforcement learning, and empirical game-theoretic analysis to compute meta-strategies for policy selection. The algorithm generalizes previous ones such as InRL, iterated best response, double oracle, and fictitious play. Then, we present a scalable implementation which reduces the memory requirement using decoupled meta-solvers. Finally, we demonstrate the generality of the resulting policies in two partially observable settings: gridworld coordination games and poker.


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    Title :

    A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning


    Contributors:
    Lanctot, M (author) / Zambaldi, V (author) / Gruslys, A (author) / Lazaridou, A (author) / Tuyls, K (author) / Perolat, J (author) / Silver, D (author) / Graepel, T (author) / Guyon, I / Luxburg, UV

    Publication date :

    2017-12-09


    Remarks:

    In: Guyon, I and Luxburg, UV and Bengio, S and Wallach, H and Fergus, R and Vishwanathan, S and Garnett, R, (eds.) Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017). Neural Information Processing Systems (NIPS): Long Beach, CA, USA. (2017)


    Type of media :

    Paper


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    DDC:    629



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