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Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning

Offline reinforcement learning (RL) paradigm provides a general recipe to convert static behavior datasets into policies that can perform better than the policy that collected the data.

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Cite this as

Jianlan Luo, Perry Dong, Jeffrey Wu, Aviral Kumar, Xinyang Geng, Sergey Levine (2024). Dataset: Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning. https://doi.org/10.57702/8zbesigl

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Additional Info

Field	Value
Created	December 2, 2024
Last update	December 2, 2024
Author	Jianlan Luo
More Authors	Perry Dong Jeffrey Wu Aviral Kumar Xinyang Geng Sergey Levine
Homepage	https://saqrl.github.io