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With the advent of large datasets, offline reinforcement learning (RL) is a promising framework for learning good decision-making policies without the need to interact with the real environment.
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Earlier work this paper cites.
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Marco Cuturi, Laetitia Meng-Papaxanthos, Yingtao Tian, Charlotte Bunne, Geoff Davis, and Olivier Teboul · 2022
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Robert Dadashi, Leonard Hussenot, Matthieu Geist, and Olivier Pietquin · 2022
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Offline Learning from Demonstrations and Unlabeled Experience
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Siddharth Reddy, Anca D. Dragan, and Sergey Levine · 2022
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Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Chelsea Finn, and Sergey Levine · 2022
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