DR-SAC is the first actor-critic distributionally robust RL algorithm for offline continuous control that derives a convergent robust soft policy iteration and reports up to 9.8x higher rewards than SAC under perturbations.
Distributionally robust model-based offline reinforcement learning with near-optimal sample complexity.Journal of Machine Learning Research, 25(200):1–91, 2024
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DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
DR-SAC is the first actor-critic distributionally robust RL algorithm for offline continuous control that derives a convergent robust soft policy iteration and reports up to 9.8x higher rewards than SAC under perturbations.