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Computer Science > Artificial Intelligence

arXiv:2112.03178 (cs)
[Submitted on 6 Dec 2021]

Title:Player of Games

Authors:Martin Schmid, Matej Moravcik, Neil Burch, Rudolf Kadlec,
Josh Davidson, Kevin Waugh, Nolan Bard, Finbarr Timbers, Marc Lanctot
, Zach Holland, Elnaz Davoodi, Alden Christianson, Michael Bowling
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    Abstract: Games have a long history of serving as a benchmark for
    progress in artificial intelligence. Recently, approaches using
    search and learning have shown strong performance across a set of
    perfect information games, and approaches using game-theoretic
    reasoning and learning have shown strong performance for specific
    imperfect information poker variants. We introduce Player of
    Games, a general-purpose algorithm that unifies previous
    approaches, combining guided search, self-play learning, and
    game-theoretic reasoning. Player of Games is the first algorithm
    to achieve strong empirical performance in large perfect and
    imperfect information games -- an important step towards truly
    general algorithms for arbitrary environments. We prove that
    Player of Games is sound, converging to perfect play as available
    computation time and approximation capacity increases. Player of
    Games reaches strong performance in chess and Go, beats the
    strongest openly available agent in heads-up no-limit Texas
    hold'em poker (Slumbot), and defeats the state-of-the-art agent
    in Scotland Yard, an imperfect information game that illustrates
    the value of guided search, learning, and game-theoretic
    reasoning.

Subjects: Artificial Intelligence (cs.AI); Computer Science and Game
          Theory (cs.GT); Machine Learning (cs.LG)
Cite as:  arXiv:2112.03178 [cs.AI]
          (or arXiv:2112.03178v1 [cs.AI] for this version)

Submission history

From: Marc Lanctot [view email]
[v1] Mon, 6 Dec 2021 17:16:24 UTC (7,469 KB)
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