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.
Q(s, a)−E[r]−γ·sup β≥0 −βlog Ep0s,a exp −V(s ′) β −βδ # . If using ERM method, the empirical Bellman residual is bLQ := 1 N NX i=1
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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.