23 citations · 32 across the 10 of their papers we have counts for
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cs.LG2023
Robust Safe Reinforcement Learning under Adversarial Disturbances
Zeyang Li, Chuxiong Hu, Shengbo Eben Li +2
Safety is a primary concern when applying reinforcement learning to real-world control tasks, especially in the presence of external disturbances. However, existing safe reinforcem…
cs.LG2023
Bridging the Gap between Newton-Raphson Method and Regularized Policy Iteration
Zeyang Li, Chuxiong Hu, Yunan Wang +4
Regularization is a cornerstone of modern reinforcement learning. Regularized policy iteration (RPI) provides a fundamental scheme for solving regularized Markov decision processes…
cs.LG2023
Safe Reinforcement Learning with Dual Robustness
Zeyang Li, Chuxiong Hu, Yunan Wang +2
Reinforcement learning (RL) agents are vulnerable to adversarial disturbances, which can deteriorate task performance or compromise safety specifications. Existing methods either a…