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, a general-purpose algorithm that unifies previous approaches, combining guided search, self-play… See more.
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.
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