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Leveraging Offline Data in Online Reinforcement Learning. (arXiv:2211.04974v1 [cs.LG])
Nov. 10, 2022, 2:11 a.m. | Andrew Wagenmaker, Aldo Pacchiano
cs.LG updates on arXiv.org arxiv.org
Two central paradigms have emerged in the reinforcement learning (RL)
community: online RL and offline RL. In the online RL setting, the agent has no
prior knowledge of the environment, and must interact with it in order to find
an $\epsilon$-optimal policy. In the offline RL setting, the learner instead
has access to a fixed dataset to learn from, but is unable to otherwise
interact with the environment, and must obtain the best policy it can from this
offline data. …
arxiv data offline online reinforcement learning reinforcement reinforcement learning
More from arxiv.org / cs.LG updates on arXiv.org
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