# Explaining Reinforcement Learning: A Survey

- **Authors**: P. He, K. Y. K. Lui, et al.
- **Year**: 2023
- **Summary**: This survey provides a comprehensive overview of methods for explaining the policies of reinforcement learning (RL) agents. It categorizes techniques for explaining why an agent chose a particular action, what it is trying to achieve, and how its policy works. This is essential for understanding and trusting autonomous agents that learn from experience.
- **Link**: https://arxiv.org/abs/2305.15623
