强化学习的可解释方法分类研究

强化学习能够在动态复杂环境中实现自主学习，这使其在法律、医学、金融等领域有着广泛应用。但强化学习仍面临着全局状态空间不可观测、对奖励函数强依赖和因果关系不确定等诸多问题，导致其可解释性弱，严重影响其在相关领域的推广，会遭遇诸如难以判断决策是否违反社会法律道德的要求，是否准确及值得信任等的限制。为了进一步了解强化学习可解释性研究现状，从可解释模型、可解释策略、环境交互、可视化等方面展开讨论。基于此，对强化学习可解释性研究现状进行系统论述，对其可解释方法进行归类阐述，最后提出强化学习可解释性的未来发展方向。

Classification study of interpretable methods for reinforcement learning

Reinforcement learning can achieve autonomous learning in dynamic and complex environments, which makes it widely used in fields such as law, medicine, and finance. However, reinforcement learning still faces many problems such as the unobservable global state space, strong dependence on the reward function, and uncertain causality, which results in its weak interpretability, seriously affecting its promotion in related fields. It will encounter limitations such as difficulty in judging whether the decision-making violates social legal and moral requirements, whether it is accurate and trustworthy, etc. In order to further understand the current status of interpretability research in reinforcement learning, this article discussed from the aspects of interpretable models, interpretable strategies, environment interaction and visualization, etc. Based on these, this article systematically discussed the research status of reinforcement learning interpretability, classified and explained its explainable methods, and finally proposed the future development direction of reinforcement learning interpretability.