Chenjie Mao

2 accepted papers

2026

Rethinking the Hardness of PbRL: A Provable General Regret Bound

Chenjie Mao, Yi Fan, Ning Zhang, Chongjie Zhang

ICML 2026poster

This paper studies \emph{preference-based reinforcement learning} (PbRL), where agents learn from comparative, trajectory-level feedback rather than numeric rewards. While PbRL has seen rapid empirical and theoretical progress, existing analyses are largely confined to restricted settings and fail t…

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2024

On the Role of General Function Approximation in Offline Reinforcement Learning

Chenjie Mao, Qiaosheng Zhang, Zhen Wang, Xuelong Li

ICLR 2024spotlight

We study offline reinforcement learning (RL) with general function approximation. General function approximation is a powerful tool for algorithm design and analysis, but its adaptation to offline RL encounters several challenges due to varying approximation targets and assumptions that blur the rea…

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