March 13, 2024, 4:41 a.m. | Chengxing Jia, Fuxiang Zhang, Yi-Chen Li, Chen-Xiao Gao, Xu-Hui Liu, Lei Yuan, Zongzhang Zhang, Yang Yu

cs.LG updates on

arXiv:2403.07261v1 Announce Type: new
Abstract: Offline meta-reinforcement learning (OMRL) proficiently allows an agent to tackle novel tasks while solely relying on a static dataset. For precise and efficient task identification, existing OMRL research suggests learning separate task representations that be incorporated with policy input, thus forming a context-based meta-policy. A major approach to train task representations is to adopt contrastive learning using multi-task offline data. The dataset typically encompasses interactions from various policies (i.e., the behavior policies), thus providing a …

abstract adversarial agent arxiv augmentation context cs.lg data dataset identification meta novel offline policy reinforcement reinforcement learning representation representation learning research tasks type via

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