Distributionally Robust Self Paced Curriculum Reinforcement Learning

• URL: http://arxiv.org/abs/2511.05694v2
• Date: Thu, 13 Nov 2025 01:18:08 GMT
• Title: Distributionally Robust Self Paced Curriculum Reinforcement Learning
• Authors: Anirudh Satheesh, Keenan Powell, Vaneet Aggarwal,
• Abstract summary: We propose Distributionally Robust Self-Paced Curriculum Reinforcement Learning (DR-SPCRL)<n>DR-SPCRL adaptively schedules the robustness budget according to the agent's progress, enabling a balance between nominal and robust performance.<n> Empirical results across multiple environments demonstrate that DR-SPCRL not only stabilizes training but also achieves a superior robustness-performance trade-off.
• Score: 42.51809641161819
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 $ε$. However, fixing $ε$ results in a tradeoff between performance and robustness: small values yield high nominal performance but weak robustness, while large values can result in instability and overly conservative policies. We propose Distributionally Robust Self-Paced Curriculum Reinforcement Learning (DR-SPCRL), a method that overcomes this limitation by treating $ε$ as a continuous curriculum. DR-SPCRL adaptively schedules the robustness budget according to the agent's progress, enabling a balance between nominal and robust performance. Empirical results across multiple environments demonstrate that DR-SPCRL not only stabilizes training but also achieves a superior robustness-performance trade-off, yielding an average 11.8\% increase in episodic return under varying perturbations compared to fixed or heuristic scheduling strategies, and achieving approximately 1.9$\times$ the performance of the corresponding nominal RL algorithms.

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