Abstract
Reinforcement learning (RL) is increasingly applied in complex, safety-critical domains, yet the lack of rigorous behavioral guarantees for neural network-based policies remains a major barrier to deployment. Recent advances in policy expressiveness and scale have intensified this challenge, leading to a rapidly growing but conceptually fragmented body of work on RL policy verification. This survey provides a unifying perspective on RL verification methods. We introduce a taxonomy that clarifies relationships among existing approaches along three axes:
- Verification Paradigm (formal versus probabilistic)
- Temporal Scope (step-wise versus multi-step)
- Strength of Guarantees
Beyond taxonomy, we unify underlying theoretical foundations, make implicit assumptions and limitations explicit, and identify emerging directions.
Blogger's Review: This paper delves into the complexity and diversity of reinforcement learning policy verification, highlighting the necessity of applying RL in safety-critical domains. The taxonomy provides a clear framework for researchers, facilitating deeper exploration and resolution of current challenges.