June 11, 2024, 4:47 a.m. | Davide Corsi, Guy Amir, Andoni Rodriguez, Cesar Sanchez, Guy Katz, Roy Fox

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.06507v1 Announce Type: new
Abstract: In recent years, Deep Reinforcement Learning (DRL) has emerged as an effective approach to solving real-world tasks. However, despite their successes, DRL-based policies suffer from poor reliability, which limits their deployment in safety-critical domains. As a result, various methods have been put forth to address this issue by providing formal safety guarantees. Two main approaches include shielding and verification. While shielding ensures the safe behavior of the policy by employing an external online component (i.e., …

abstract arxiv cs.lg deployment domains however issue policies reinforcement reinforcement learning reliability safety safety-critical tasks type verification world

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