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arXiv AI By Alexander W. Goodall, Francesco Belardinelli

RECREG: Recovery-based Shielding for Regular Safety Properties in Reinforcement Learning

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The paper introduces RecReg, a safe reinforcement learning framework that enforces non‑Markovian regular safety properties through recovery‑based shielding. It provides theoretical foundations and rigorous safety and convergence guarantees in the tabular setting, and demonstrates empirical effectiveness using the MASA‑Safe‑RL library. The authors also outline an extension of RecReg to deep RL with state‑of‑the‑art model‑based methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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