8 citations · 22 across the 16 of their papers we have counts for
5 papers · 1 filter
Optimal Multi-Distribution Learning
Zihan Zhang, Wenhao Zhan, Yuxin Chen +2
Multi-distribution learning (MDL), which seeks to learn a shared model that minimizes the worst-case risk across distinct data distributions, has emerged as a unified framework…
Provably Efficient CVaR RL in Low-rank MDPs
Yulai Zhao, Wenhao Zhan, Xiaoyan Hu +4
We study risk-sensitive Reinforcement Learning (RL), where we aim to maximize the Conditional Value at Risk (CVaR) with a fixed risk tolerance . Prior theoretical work studying…
Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning
Gen Li, Wenhao Zhan, Jason D. Lee +2
This paper studies tabular reinforcement learning (RL) in the hybrid setting, which assumes access to both an offline dataset and online interactions with the unknown environment.…
Provable Reward-Agnostic Preference-Based Reinforcement Learning
Wenhao Zhan, Masatoshi Uehara, Wen Sun +1
Preference-based Reinforcement Learning (PbRL) is a paradigm in which an RL agent learns to optimize a task using pair-wise preference-based feedback over trajectories, rather than…
Provable Offline Preference-Based Reinforcement Learning
Wenhao Zhan, Masatoshi Uehara, Nathan Kallus +2
In this paper, we investigate the problem of offline Preference-based Reinforcement Learning (PbRL) with human feedback where feedback is available in the form of preference betwee…