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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 th
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