Reinforcement Learning Interpretability Methods and Decision Making Methods under Constraints

Reinforcement learning (RL), as a core technology of artificial intelligence, has shown strong potential in the fields of robotics, games and autonomous driving. However, the "black box" nature of deep RL models leads to a lack of transparency in the decision-making process, making it difficult for users to understand and trust the agent behavior of RL models, and the uninterpretability of decisions may cause serious consequences in sensitive fields such as healthcare and finance. At the same time, because traditional RL pursues maximum reward and result models often ignore fairness, leading to policy bias, which affects the group's rights. So this article will summarize from the perspective of two key transparency and fairness of RL as summarized in the paper: one is based on the interpretability of the decision-making method, using the causal analysis and partial interpretation and visualization tools to make decisions transparent; Second, the decision-making method based on the constraint conditions, through multi-objective optimization and gradually constraints ensure the decision unfair. This review covers the methodologies, experimental results and limitations of representative literature in recent years. The significance of this paper is to systematically integrate these methods, reveal the interaction challenges of transparency and fairness, promote the development of more reliable RL systems, and look forward to future directions to help promote the ethical deployment and sustainable innovation of RL in social applications.