AI RESEARCH
Rainbow-DemoRL: Combining Improvements in Demonstration-Augmented Reinforcement Learning
arXiv CS.LG
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ArXi:2603.27400v1 Announce Type: cross Several approaches have been proposed to improve the sample efficiency of online reinforcement learning (RL) by leveraging nstrations collected offline. The offline data can be used directly as transitions to optimize RL objectives, or offline policy and value functions can first be learned from the data and then used for online finetuning or to provide reference actions.