114 citations · 167 across the 16 of their papers we have counts for
Showing 2021 · cs.LGShow all
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cs.LG2021★ 6 cited
Revisiting Design Choices in Offline Model-Based Reinforcement Learning
Cong Lu, Philip J. Ball, Jack Parker-Holder +2
Offline reinforcement learning enables agents to leverage large pre-collected datasets of environment transitions to learn control policies, circumventing the need for potentially…
cs.LG2021★ 9 cited
Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment
Philip J. Ball, Cong Lu, Jack Parker-Holder +1
Reinforcement learning from large-scale offline datasets provides us with the ability to learn policies without potentially unsafe or impractical exploration. Significant progress…