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This survey addresses the critical gap in deploying reinforcement learning within safety-critical systems by unifying fragmented verification methods into a clear taxonomy based on paradigm, temporal scope, and guarantee strength. By explicitly mapping theoretical foundations and limitations across formal versus probabilistic approaches, it provides essential direction for researchers aiming to establish rigorous behavioral guarantees for next-generation AI policies.
A Survey on the Verification of Reinforcement Learning Policies
arXiv:2607.16210v1 Announce Type: new 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.
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.
Artificial Intelligence (cs.AI)
arXiv:2607.16210 [cs.AI]
(or arXiv:2607.16210v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.16210 Focus to learn more arXiv-issued DOI via DataCite
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