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Parallel Monte-Carlo Tree Search
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BibTeX
@MISC{Chaslot_parallelmonte-carlo,
author = {Guillaume M. J-b. Chaslot and Mark H. M. Win and H. Jaap Van Den Herik},
title = {Parallel Monte-Carlo Tree Search},
year = {}
}
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Abstract
Abstract. Monte-Carlo Tree Search (MCTS) is a new best-first search method that started a revolution in the field of Computer Go. Parallelizing MCTS is an important way to increase the strength of any Go program. In this article, we discuss three parallelization methods for MCTS: leaf parallelization, root parallelization, and tree parallelization. To be effective tree parallelization requires two techniques: adequately handling of (1) local mutexes and (2) virtual loss. Experiments in 13×13 Go reveal that in the program Mango root parallelization may lead to the best results for a specific time setting and specific program parameters. However, as soon as the selection mechanism is able to handle more adequately the balance of exploitation and exploration, tree parallelization should have attention too and could become a second choice for parallelizing MCTS. Preliminary experiments on the smaller 9 × 9 board provide promising prospects for tree parallelization. 1
Keyphrases
tree parallelization parallel monte-carlo tree search board provide leaf parallelization parallelization method monte-carlo tree search important way preliminary experiment second choice go program effective tree parallelization selection mechanism specific program parameter specific time setting new best-first search method program mango local mutexes root parallelization go reveal virtual loss computer go
