arXiv Machine Learning July 9, 2026 By Xulin Chen, Ruipeng Liu, Zhenyu Gan, Garrett E. Katz

Lipschitz-Regularized Critics Lead to Policy Robustness Against Transition Dynamics Uncertainty

Read the original on arXiv Machine Learning →

arXiv:2404. 13879v5 Announce Type: replace Abstract: Uncertainties in transition dynamics pose a critical challenge in reinforcement learning (RL), often resulting in performance degradation of trained policies when deployed on hardware.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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