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Pengcheng You

2 accepted papers

2025

Efficient Discovery of Pareto Front for Multi-Objective Reinforcement Learning

ICLR 2025poster

Multi-objective reinforcement learning (MORL) excels at handling rapidly changing preferences in tasks that involve multiple criteria, even for unseen preferences. However, previous dominating MORL methods typically generate a fixed policy set or preference-conditioned policy through multiple traini…

Cited by 0SourcePDFScholar
2025

Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics Data

AISTATS 2025oral

Online reinforcement learning (RL) typically requires online interaction data to learn a policy for a target task, but collecting such data can be high-stakes. This prompts interest in leveraging historical data to improve sample efficiency. The historical data may come from outdated or related sour…

Cited by 0SourcecodeScholar