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mcts-NC: A thorough GPU parallelization of Monte Carlo Tree Search implemented in Python via ***

SOFTWAREX 2025年 30卷

作者： Klesk, Przemyslaw West Pomeranian Univ Technol Szczecin Fac Comp Sci & Informat Technol Ul Zolnierska 49 PL-71210 Szczecin Poland

With CUDA computational model in mind, we introduce mcts-NC (Monte Carlo Tree ***). It contains four, fast-operating and thoroughly parallel, variants of the mcts algorithm. The design of mcts-NC combines three parallelization levels (leaf/root/tree parallelizations). Additionally, all algorithmic stages-selections, expansions, playouts, backups-employ multiple GPU threads. We apply suitable reduction patterns to carry out summations or max/ argmax operations. The implementation uses very few device-host memory transfers, no atomic operations (is lock-free), and takes advantage of threads cooperation. In the mathematical part of this article, we demonstrate how the confidence bounds on estimated action values become tightened by both the number of independent concurrent playouts and the number of independent concurrent trees. The experimental part reports the performance of mcts-NC on two game examples: Connect4 and Gomoku. All computational results can be exactly reproduced.

关键词： mcts parallelization CUDA Python Numba Bounds on action values estimates

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Monte-Carlo Tree Search with Tree Shape Control 1

Monte-Carlo Tree Search with Tree Shape Control

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1st IEEE Ukraine Conference on Electrical and Computer Engineering (UKRCON)

作者： Marchenko, Oleksandr I. Marchenko, Oleksii O. Natl Tech Univ Ukraine Igor Sikorsky Kyiv Polytech Inst Dept Syst Programming & Specialized Comp Syst Kiev Ukraine

ISBN: (纸本)9781509030064

The paper, basing on analysis of the Monte-Carlo Tree Search (mcts) method and specific features of its behavior for various cases of usage, proposes a new variant of the method, which was called as Monte-Carlo Tree Search with Tree Shape Control (mcts-TSC) and which uses original Depth-Width Criteria (DWCs) for both tree shape estimation and control during search and for estimation and selection of potentially better options for search continuation. Proposed Tree Shape Control (TSC) technique can be used with some other tuning, pruning, and learning techniques. Besides, it can provide better scheduling of mcts parallelization.

关键词： Monte-Carlo Tree Search (mcts) game tree mcts improvement techniques exploitation-exploration dilemma tree shape control mcts parallelization

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Monte-Carlo Tree Search with Tree Shape Control

Monte-Carlo Tree Search with Tree Shape Control

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IEEE Ukraine Conference on Electrical and Computer Engineering

作者： Oleksandr I. Marchenko Oleksii O. Marchenko Department of System Programming and Specialized Computer Systems National Technical University of Ukraine "Igor Sikorsky Kyiv polytechnic institute"

ISBN: (纸本)9781509030071

关键词： Monte-Carlo Tree Search (mcts) Game tree mcts improvement techniques Exploitation-exploration dilemma Tree shape control mcts parallelization

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