524 citations · 2.7k across the 55 of their papers we have counts for
6 papers · 1 filter
Autonomous Reinforcement Learning: Formalism and Benchmarking
Archit Sharma, Kelvin Xu, Nikhil Sardana +4
Reinforcement learning (RL) provides a naturalistic framing for learning through trial and error, which is appealing both because of its simplicity and effectiveness and because of…
AW-Opt: Learning Robotic Skills with Imitation and Reinforcement at Scale
Yao Lu, Karol Hausman, Yevgen Chebotar +8
Robotic skills can be learned via imitation learning (IL) using user-provided demonstrations, or via reinforcement learning (RL) using large amountsof autonomously collected experi…
Conservative Data Sharing for Multi-Task Offline Reinforcement Learning
Tianhe Yu, Aviral Kumar, Yevgen Chebotar +3
Offline reinforcement learning (RL) algorithms have shown promising results in domains where abundant pre-collected data is available. However, prior methods focus on solving indiv…
Autonomous Reinforcement Learning via Subgoal Curricula
Archit Sharma, Abhishek Gupta, Sergey Levine +2
Reinforcement learning (RL) promises to enable autonomous acquisition of complex behaviors for diverse agents. However, the success of current reinforcement learning algorithms is…
MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale
Dmitry Kalashnikov, Jacob Varley, Yevgen Chebotar +5
General-purpose robotic systems must master a large repertoire of diverse skills to be useful in a range of daily tasks. While reinforcement learning provides a powerful framework…
Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills
Yevgen Chebotar, Karol Hausman, Yao Lu +8
We consider the problem of learning useful robotic skills from previously collected offline data without access to manually specified rewards or additional online exploration, a se…