2 citations · 4 across the 14 of their papers we have counts for
4 papers · 1 filter
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation
Heyang Zhao, Jiafan He, Quanquan Gu
The exploration-exploitation dilemma has been a central challenge in reinforcement learning (RL) with complex model classes. In this paper, we propose a new algorithm, Monotonic Q-…
Corruption-Robust Offline Reinforcement Learning with General Function Approximation
Chenlu Ye, Rui Yang, Quanquan Gu +1
We investigate the problem of corruption robustness in offline reinforcement learning (RL) with general function approximation, where an adversary can corrupt each sample in the of…
Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning
Qiwei Di, Heyang Zhao, Jiafan He +1
Offline reinforcement learning (RL), where the agent aims to learn the optimal policy based on the data collected by a behavior policy, has attracted increasing attention in recent…
Variance-Aware Regret Bounds for Stochastic Contextual Dueling Bandits
Qiwei Di, Tao Jin, Yue Wu +3
Dueling bandits is a prominent framework for decision-making involving preferential feedback, a valuable feature that fits various applications involving human interaction, such as…