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.
Safe reinforcement learning using wasserstein distributionally robust mpc and chance constraint.IEEE Access, 10:130058– 130067
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.