A Survey on the Verification of Reinforcement Learning Policies

• URL: http://arxiv.org/abs/2607.16210v1
• Date: Thu, 14 May 2026 12:32:57 GMT
• Title: A Survey on the Verification of Reinforcement Learning Policies
• Abstract summary: Reinforcement learning is increasingly applied in complex, safety-critical domains.<n>The lack of rigorous behavioral guarantees for neural network-based policies remains a major barrier to deployment.<n>This survey provides a unifying perspective on RL verification methods.
• Score: 7.245989727730095
• License: http://creativecommons.org/licenses/by/4.0/
• 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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