D4RL: Datasets for Deep Data-Driven Reinforcement Learning

15 Apr 2020 • Justin Fu • Aviral Kumar • Ofir Nachum • George Tucker • Sergey Levine

The offline reinforcement learning (RL) setting (also known as full batch RL), where a policy is learned from a static dataset, is compelling as progress enables RL methods to take advantage of large, previously-collected datasets, much like how the rise of large datasets has fueled results in supervised learning. However, existing online RL benchmarks are not tailored towards the offline setting and existing offline RL benchmarks are restricted to data generated by partially-trained agents, making progress in offline RL difficult to measure... (read more)

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