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• W2208149249 abstract "© Copyright 2014. Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Self-play reinforcement learning has proved to be succ essful in many perfect information two-player games. However, research carrying over its theoretical guaranI ces and practical success to games of imperfect inform ation has been lacking. In this paper, we evaluate self- play Monte-Carlo Tree Search (MCTS) in limit Texas Hold'em and Kuhn poker. We introduce a variant of the established UCB algorithm and provide first empinc al results demonstrating its ability to find approximate Nash equilibria." @default.
• W2208149249 created "2016-06-24" @default.
• W2208149249 creator A5066255933 @default.
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• W2208149249 date "2014-01-01" @default.
• W2208149249 modified "2023-09-24" @default.
• W2208149249 title "Self-play Monte-Carlo tree search in computer poker" @default.
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