465 citations · 4.4k across the 130 of their papers we have counts for
29 papers · 1 filter
Parallel Training of Deep Networks with Local Updates
Michael Laskin, Luke Metz, Seth Nabarro +5
Deep learning models trained on large data sets have been widely successful in both vision and language domains. As state-of-the-art deep learning architectures have continued to g…
Reset-Free Lifelong Learning with Skill-Space Planning
Kevin Lu, Aditya Grover, Pieter Abbeel +1
The objective of lifelong reinforcement learning (RL) is to optimize agents which can continuously adapt and interact in changing environments. However, current RL approaches fail…
Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning
Younggyo Seo, Kimin Lee, Ignasi Clavera +3
Model-based reinforcement learning (RL) has shown great potential in various control tasks in terms of both sample-efficiency and final performance. However, learning a generalizab…
LaND: Learning to Navigate from Disengagements
Gregory Kahn, Pieter Abbeel, Sergey Levine
Consistently testing autonomous mobile robots in real world scenarios is a necessary aspect of developing autonomous navigation systems. Each time the human safety monitor disengag…
Robust Reinforcement Learning using Adversarial Populations
Eugene Vinitsky, Yuqing Du, Kanaad Parvate +3
Reinforcement Learning (RL) is an effective tool for controller design but can struggle with issues of robustness, failing catastrophically when the underlying system dynamics are…
Visual Imitation Made Easy
Sarah Young, Dhiraj Gandhi, Shubham Tulsiani +3
Visual imitation learning provides a framework for learning complex manipulation behaviors by leveraging human demonstrations. However, current interfaces for imitation such as kin…