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

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

Keywords: Deep Reinforcement Learning, Monte Carlo tree search, MuZero, efficiency optimization, reanalyze

TL;DR: Using a backward-view reanalyze process inspired by one-armed bandit model and simpified framework to boost MCTS-based algorithms.

Abstract:

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.

Primary Area: reinforcement learning

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Submission Number: 8610