AI RESEARCH
DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
arXiv CS.LG
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ArXi:2506.12622v2 Announce Type: replace Deep reinforcement learning (RL) has achieved remarkable success, yet its deployment in real-world scenarios is often limited by vulnerability to environmental uncertainties. Distributionally robust RL (DR-RL) algorithms have been proposed to resolve this challenge, but existing approaches are largely restricted to value-based methods in tabular settings. In this work, we