AGENTIC-SYSTEMSJuly 21, 20261 min read

A Survey on the Verification of Reinforcement Learning Policies

While Reinforcement Learning is rapidly expanding into safety-critical domains, the lack of rigorous guarantees remains a barrier. A new survey unifies RL policy verification methods into a structured taxonomy, providing a comprehensive framework for trustworthy AI.

Computer Science > Artificial Intelligence

Title:A Survey on the Verification of Reinforcement Learning Policies

View PDF HTML (experimental)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), and guarantees strength. Beyond taxonomy, we unify underlying theoretical foundations, make implicit assumptions and limitations explicit, and identify emerging directions.

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Source: arXiv cs.AI Recent

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