Playout Policy Adaptation for Games

被引：7

作者：

Cazenave, Tristan ^{[1
]}

机构：

[1] Univ Paris 09, LAMSADE, Paris, France

来源：

ADVANCES IN COMPUTER GAMES, ACG 2015 | 2015年 / 9525卷

关键词：

D O I：

10.1007/978-3-319-27992-3_3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Monte-Carlo Tree Search (MCTS) is the state of the art algorithm for General Game Playing (GGP). We propose to learn a playout policy online so as to improve MCTS for GGP. We test the resulting algorithm named Playout Policy Adaptation (PPA) on Atarigo, Breakthrough, Misere Breakthrough, Domineering, Misere Dominee-ring, Go, Knightthrough, Misere Knightthrough, Nogo and Misere Nogo. For most of these games, PPA is better than UCT with a uniform random playout policy, with the notable exceptions of Go and Nogo.

引用

页码：20 / 28

页数：9

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