Pruning Playouts in Monte-Carlo Tree Search for the Game of Havannah

被引：5

作者：

Dugueperoux, Joris ^{[1
]}

Mazyad, Ahmad ^{[1
]}

Teytaud, Fabien ^{[1
]}

Dehos, Julien ^{[1
]}

机构：

[1] ULCO, LISIC, Calais, France

来源：

COMPUTERS AND GAMES, CG 2016 | 2016年 / 10068卷

关键词：

GOOD-REPLY POLICY;

D O I：

10.1007/978-3-319-50935-8_5

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Monte-Carlo Tree Search (MCTS) is a popular technique for playing multi-player games. In this paper, we propose a new method to bias the playout policy of MCTS. The idea is to prune the decisions which seem "bad" (according to the previous iterations of the algorithm) before computing each playout. Thus, the method evaluates the estimated "good" moves more precisely. We have tested our improvement for the game of Havannah and compared it to several classic improvements. Our method outperforms the classic version of MCTS (with the RAVE improvement) and the different playout policies of MCTS that we have experimented.

引用

页码：47 / 57

页数：11

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