Certifying Almost-Sure Reachability in Unknown MDPs
{\em Specification-Guided Reinforcement Learning (RL)} has largely concerned itself with \emph{learning} an optimal policy against a formal specification in an unknown {\em Markov Decision Process (MDP)}. We take up the dual question. Given a policy already in hand, can one \emph{verify} that it is correct against a formal specification in an unknown MDP, using sampling access alone? Such a question arises naturally in safety-critical systems. We investigate it for the {\em almost-sure reachability property}, whose correctness is witnessed by a {\em ranking supermartingale (RSM)} certificate. Our first finding is negative. In the absence of any further information about the MDP, one can n...
| Authors | S Akshay, Suguman Bansal, Gaurav Mahla |
|---|---|
| Primary category | cs.LO |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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