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cs.LG2026
From Reward-Free Representations to Preferences: Rethinking Offline Preference-Based Reinforcement Learning
Jun-Jie Yang, Chia-Heng Hsu, Kui-Yuan Chen +1
Preference-based reinforcement learning (PbRL) avoids explicit reward engineering by learning from pairwise human preference feedback. Existing offline PbRL methods typically follo…
cs.LG2026
A Reward-Free Viewpoint on Multi-Objective Reinforcement Learning
Ying-Tu Chen, Wei Hung, Bing-Shu Wu +2
Many sequential decision-making tasks involve optimizing multiple conflicting objectives, requiring policies that adapt to different user preferences. In multi-objective reinforcem…
cs.LG2026
Semi-Supervised Cross-Domain Imitation Learning
Li-Min Chu, Kai-Siang Ma, Ming-Hong Chen +1
Cross-domain imitation learning (CDIL) accelerates policy learning by transferring expert knowledge across domains, which is valuable in applications where the collection of expert…