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Explicit Lipschitz Value Estimation Enhances Policy Robustness Against Perturbation
April 23, 2024, 4:42 a.m. | Xulin Chen, Ruipeng Liu, Garrett E. Katz
cs.LG updates on arXiv.org arxiv.org
Abstract: In robotic control tasks, policies trained by reinforcement learning (RL) in simulation often experience a performance drop when deployed on physical hardware, due to modeling error, measurement error, and unpredictable perturbations in the real world. Robust RL methods account for this issue by approximating a worst-case value function during training, but they can be sensitive to approximation errors in the value function and its gradient before training is complete. In this paper, we hypothesize that …
abstract arxiv case control cs.lg error experience hardware issue measurement modeling performance policies policy reinforcement reinforcement learning robotic robust robustness simulation tasks type value world
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