2 citations · 4 across the 14 of their papers we have counts for
4 papers · 1 filter
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…
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…
Logarithmic Regret for Online KL-Regularized Reinforcement Learning
Heyang Zhao, Chenlu Ye, Wei Xiong +2
Recent advances in Reinforcement Learning from Human Feedback (RLHF) have shown that KL-regularization plays a pivotal role in improving the efficiency of RL fine-tuning for large…
Towards a Sharp Analysis of Offline Policy Learning for -Divergence-Regularized Contextual Bandits
Qingyue Zhao, Kaixuan Ji, Heyang Zhao +2
Many offline reinforcement learning algorithms are underpinned by -divergence regularization, but their sample complexity *defined with respect to regularized objectives* still…