PROGRESSIVE STRATEGIES FOR MONTE-CARLO TREE SEARCH

被引：193

作者：

Chaslot, Guillaume M. J-B. ^{[1
]}

Winands, Mark H. M. ^{[1
]}

Van den Herik, H. Jaap ^{[1
]}

Uiterwijk, Jos W. H. M. ^{[1
]}

Bouzy, Bruno ^{[2
]}

机构：

[1] Univ Maastricht, Fac Humanities & Sci, MICC IKAT Games & AI Grp, POB 616, NL-6200 MD Maastricht, Netherlands

[2] Univ Paris 5 Descartes, Ctr Rech Informat Paris 5, F-75270 Paris 06, France

来源：

NEW MATHEMATICS AND NATURAL COMPUTATION | 2008年 / 4卷 / 03期

关键词：

Monte-Carlo Tree Search; heuristic search; Computer Go;

D O I：

10.1142/S1793005708001094

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

Monte-Carlo Tree Search (MCTS) is a new best-first search guided by the results of Monte-Carlo simulations. In this article, we introduce two progressive strategies for MCTS, called progressive bias and progressive unpruning. They enable the use of relatively time-expensive heuristic knowledge without speed reduction. Progressive bias directs the search according to heuristic knowledge. Progressive unpruning first reduces the branching factor, and then increases it gradually again. Experiments assess that the two progressive strategies significantly improve the level of our Go program Mango. Moreover, we see that the combination of both strategies performs even better on larger board sizes.

引用

页码：343 / 357

页数：15

共 50 条

[1] Monte-Carlo Tree Search for Logistics
Edelkamp, Stefan
Gath, Max
Greulich, Christoph
Humann, Malte
Herzog, Otthein
Lawo, Michael
[J]. COMMERCIAL TRANSPORT, 2016, : 427 - 440
[2] Parallel Monte-Carlo Tree Search
Chaslot, Guillaume M. J. -B.
Winands, Mark H. M.
van den Herik, H. Jaap
[J]. COMPUTERS AND GAMES, 2008, 5131 : 60 - +
[3] Monte-Carlo Tree Search Solver
Winands, Mark H. M.
Bjornsson, Yngvi
Saito, Jahn-Takeshi
[J]. COMPUTERS AND GAMES, 2008, 5131 : 25 - +
[4] Monte-Carlo Tree Search with Tree Shape Control
Marchenko, Oleksandr I.
Marchenko, Oleksii O.
[J]. 2017 IEEE FIRST UKRAINE CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (UKRCON), 2017, : 812 - 817
[5] Comparing Randomization Strategies for Search-Control Parameters in Monte-Carlo Tree Search
Sironi, Chiara F.
Winands, Mark H. M.
[J]. 2019 IEEE CONFERENCE ON GAMES (COG), 2019,
[6] Monte-Carlo Tree Search for Constrained POMDPs
Lee, Jongmin
Kim, Geon-Hyeong
Poupart, Pascal
Kim, Kee-Eung
[J]. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), 2018, 31
[7] Monte-Carlo Tree Search in Settlers of Catan
Szita, Istvan
Chaslot, Guillaume
Spronck, Pieter
[J]. ADVANCES IN COMPUTER GAMES, 2010, 6048 : 21 - +
[8] Monte-Carlo Tree Search: To MC or to DP?
Feldman, Zohar
Domshlak, Carmel
[J]. 21ST EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE (ECAI 2014), 2014, 263 : 321 - 326
[9] Score Bounded Monte-Carlo Tree Search
Cazenave, Tristan
Saffidine, Abdallah
[J]. COMPUTERS AND GAMES, 2011, 6515 : 93 - 104
[10] Improving Monte-Carlo Tree Search in Havannah
Lorentz, Richard J.
[J]. COMPUTERS AND GAMES, 2011, 6515 : 105 - 115

← 1 2 3 4 5 →