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• W1837153980 abstract "While Monte Carlo Tree Search (MCTS) represented a revolution in game related AI research, it is currently unfit for tasks that deal with continuous actions and (often as a consequence) game-states. Recent applications of MCTS to quasi continuous games such as no-limit Poker variants have circumvented this problem by discretizing the action or the statespace. We present Tree Learning Search (TLS) as an alternative to a priori discretization. TLS employs ideas from data stream mining to combine incremental tree induction with MCTS to construct game-state-dependent discretizations that allow MCTS to focus its sampling spread more efficiently on regions of the search space with promising returns. We evaluate TLS on global function optimization problems to illustrate its potential and show results from an early implementation on a full scale no-limit Texas Hold’em Poker bot." @default.
• W1837153980 created "2016-06-24" @default.
• W1837153980 creator A5002817190 @default.
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• W1837153980 date "2011-09-01" @default.
• W1837153980 modified "2023-09-25" @default.
• W1837153980 title "Automatic discretization of actions and states in Monte-Carlo tree search" @default.
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