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20212026
most citedNearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes

4 citations · 9 across the 17 of their papers we have counts for

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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…

cs.LG2024

Feel-Good Thompson Sampling for Contextual Dueling Bandits

Xuheng Li, Heyang Zhao, Quanquan Gu

Contextual dueling bandits, where a learner compares two options based on context and receives feedback indicating which was preferred, extends classic dueling bandits by incorpora…