AAAI Publications, Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence

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Self-Play Monte-Carlo Tree Search in Computer Poker
Johannes Heinrich, David Silver

Last modified: 2014-06-18


Self-play reinforcement learning has proved to be successful in many perfect information two-player games. However, research carrying over its theoretical guarantees and practical success to games of imperfect information has been lacking. In this paper, we evaluate self-play Monte-Carlo Tree Search in limit Texas Hold'em and Kuhn poker. We introduce a variant of the established UCB algorithm and provide first empirical results demonstrating its ability to find approximate Nash equilibria.


Reinforcement Learning; Monte-Carlo Tree Search; Imperfect Information; Poker

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