Papers › ReZero: Boosting MCTS-based Algorithms by Backward-view and Entire-buffer Reanalyze

ReZero: Boosting MCTS-based Algorithms by Backward-view and Entire-buffer Reanalyze

25 Apr 2024arXiv:2404.16364archive 2025-07-28

Chunyu Xuan, Yazhe Niu, Yuan Pu, Shuai Hu, Yu Liu, Jing Yang

Monte Carlo Tree Search (MCTS)-based algorithms, such as MuZero and its derivatives, have achieved widespread success in various decision-making domains. These algorithms employ the reanalyze process to enhance sample efficiency from stale data, albeit at the expense of significant wall-clock time consumption. To address this issue, we propose a general approach named ReZero to boost tree search operations for MCTS-based algorithms. Specifically, drawing inspiration from the one-armed bandit model, we reanalyze training samples through a backward-view reuse technique which uses the value estimation of a certain child node to save the corresponding sub-tree search time. To further adapt to this design, we periodically reanalyze the entire buffer instead of frequently reanalyzing the mini-batch. The synergy of these two designs can significantly reduce the search cost and meanwhile guarantee or even improve performance, simplifying both data collecting and reanalyzing. Experiments conducted on Atari environments, DMControl suites and board games demonstrate that ReZero substantially improves training speed while maintaining high sample efficiency. The code is available as part of the LightZero MCTS benchmark at https://github.com/opendilab/LightZero.

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opendilab/LightZero officialmentioned in papermentioned on GitHubpytorch report

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Board GamesDecision Making

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Average PoolingBatch NormalizationConvolutionMonte-Carlo Tree SearchMuZeroPrioritized Experience ReplayReLUReZeroResidual BlockResidual ConnectionSPEED

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