With the aim of improving performance in Markov Decision Problem in an Off-Policy setting, we suggest taking inspiration from what is done in Offline Reinforcement Learning (RL). In Offline RL, it is a common practice during policy learning to maintain proximity to a reference policy to mitigate uncertainty, reduce potential policy errors, and help improve performance. We find ourselves in a different setting, yet it raises questions about whether a similar concept can be applied to enhance performance ie, whether it is possible to find a guiding policy capable of contributing to performance improvement, and how to incorporate it into our RL agent. Our attention is particularly focused on algorithms based on Monte Carlo Tree Search (MCTS) as a guide.MCTS renowned for its state-of-the-art capabilities across various domains, catches our interest due to its ability to converge to equilibrium in single-player and two-player contexts. By harnessing the power of MCTS as a guide for our RL agent, we observed a significant performance improvement, surpassing the outcomes achieved by utilizing each method in isolation. Our experiments were carried out on the Atari 100k benchmark.

本研究旨在解决离线强化学习中的Markov决策问题，提出利用引导策略来提升表现。通过将蒙特卡罗树搜索（MCTS）作为引导，研究表明这种方法显著改善了强化学习代理的性能，超越了各方法单独使用的效果。

通过引导搜索增强强化学习