3 papers
cs.LG2025
Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration
Heyang Zhao, Xingrui Yu, David M. Bossens +2
Imitation learning is a central problem in reinforcement learning where the goal is to learn a policy that mimics the expert's behavior. In practice, it is often challenging to lea…
cs.LG2025
Variance-Dependent Regret Lower Bounds for Contextual Bandits
Jiafan He, Quanquan Gu
Variance-dependent regret bounds for linear contextual bandits, which improve upon the classical regret bound to , wher…
cs.LG2024
Sharp Analysis for KL-Regularized Contextual Bandits and RLHF
Heyang Zhao, Chenlu Ye, Quanquan Gu +1
Reverse-Kullback-Leibler (KL) regularization has emerged to be a predominant technique used to enhance policy optimization in reinforcement learning (RL) and reinforcement learning…