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, albeit at the expense of
significant wall-clock time consumption. To address this issue, we propose a
general approach named ReZero to boost MCTS-based algorithms. Specifically, we
propose a new scheme that simplifies data collecting and reanalyzing, which
significantly reduces the search cost while guarantees the performance as well.
Furthermore, to accelerate each search process, we conceive a method to reuse
the subsequent information in the trajectory. The corresponding analysis
conducted on the bandit model also provides auxiliary theoretical
substantiation for our design. Experiments conducted on Atari environments and
board games demonstrates that ReZero substantially improves training speed
while maintaining high sample efficiency. The code is available as part of the
LightZero benchmark at this https URL

提供了一种名为 ReZero 的通用方法来提高基于 MCTS 的算法，在显著降低搜索成本的同时保证性能，从而大幅提高训练速度和样本效率。