D4rl: Datasets for deep data driven reinforcement learning

Justin Fu
Justin Fu
Sergey Levine
Sergey Levine

arXiv preprint arXiv:2004.07219, 2020.

Cited by: 9|Bibtex|Views13

Abstract:

The offline reinforcement learning (RL) problem, also referred to as batch RL, refers to the setting where a policy must be learned from a dataset of previously collected data, without additional online data collection. In supervised learning, large datasets and complex deep neural networks have fueled impressive progress, but in contrast...More

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