Safe Reinforcement Learning with Receding-Horizon Safety Critic and Differentiable Action Shields

Despite the Markov assumption, safety failures in robot reinforcement learning rarely occur instantaneously; they typically emerge over several steps as the policy enters unfamiliar states under disturbances and domain shift. We address this limitation by learning a receding-horizon safety critic that predicts whether a state will remain non-terminating over the next receding horizon, enabling anticipatory detection of impending locomotion failures.

초록에서 가져왔습니다. 요약을 준비 중입니다.