AI RESEARCH
Distributionally Robust Self Paced Curriculum Reinforcement Learning
arXiv CS.LG
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ArXi:2511.05694v3 Announce Type: replace A central challenge in reinforcement learning is that policies trained in controlled environments often fail under distribution shifts at deployment into real-world environments. Distributionally Robust Reinforcement Learning (DRRL) addresses this by optimizing for worst-case performance within an uncertainty set defined by a robustness budget $\epsilon