AI RESEARCH
Active Advantage-Aligned Online Reinforcement Learning with Offline Data
arXiv CS.LG
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ArXi:2502.07937v4 Announce Type: replace Online reinforcement learning (RL) enhances policies through direct interactions with the environment, but faces challenges related to sample efficiency. In contrast, offline RL leverages extensive pre-collected data to learn policies, but often produces suboptimal results due to limited data coverage. Recent efforts integrate offline and online RL in order to harness the advantages of both approaches.