25 citations · 38 across the 3 of their papers we have counts for
3 papers
cs.RO2023
Learning and Adapting Agile Locomotion Skills by Transferring Experience
Laura Smith, J. Chase Kew, Tianyu Li +5
Legged robots have enormous potential in their range of capabilities, from navigating unstructured terrains to high-speed running. However, designing robust controllers for highly…
cs.LG2023★ 13 cited
Efficient Online Reinforcement Learning with Offline Data
Philip J. Ball, Laura Smith, Ilya Kostrikov +1
Sample efficiency and exploration remain major challenges in online reinforcement learning (RL). A powerful approach that can be applied to address these issues is the inclusion of…
cs.RO2022★ 25 cited
A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning
Laura Smith, Ilya Kostrikov, Sergey Levine
Deep reinforcement learning is a promising approach to learning policies in uncontrolled environments that do not require domain knowledge. Unfortunately, due to sample inefficienc…