May 14, 2024, 4:42 a.m. | Changhong Wang, Xudong Yu, Chenjia Bai, Qiaosheng Zhang, Zhen Wang

cs.LG updates on

arXiv:2405.07223v1 Announce Type: new
Abstract: In Reinforcement Learning (RL), training a policy from scratch with online experiences can be inefficient because of the difficulties in exploration. Recently, offline RL provides a promising solution by giving an initialized offline policy, which can be refined through online interactions. However, existing approaches primarily perform offline and online learning in the same task, without considering the task generalization problem in offline-to-online adaptation. In real-world applications, it is common that we only have an offline …

abstract arxiv cs.lg ensemble exploration giving however interactions offline online reinforcement learning policy reinforcement reinforcement learning scratch solution through training type

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