MOPO: Model-based Offline Policy Optimization

27 May 2020Tianhe YuGarrett ThomasLantao YuStefano ErmonJames ZouSergey LevineChelsea FinnTengyu Ma

Offline reinforcement learning (RL) refers to the problem of learning policies entirely from a batch of previously collected data. This problem setting is compelling, because it offers the promise of utilizing large, diverse, previously collected datasets to acquire policies without any costly or dangerous active exploration, but it is also exceptionally difficult, due to the distributional shift between the offline training data and the learned policy... (read more)

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