297 citations · 1.5k across the 27 of their papers we have counts for
3 papers · 1 filter
In-context Reinforcement Learning with Algorithm Distillation
Michael Laskin, Luyu Wang, Junhyuk Oh +11
We propose Algorithm Distillation (AD), a method for distilling reinforcement learning (RL) algorithms into neural networks by modeling their training histories with a causal seque…
A Generalist Agent
Scott Reed, Konrad Zolna, Emilio Parisotto +17
Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we…
Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors
Steven Bohez, Saran Tunyasuvunakool, Philemon Brakel +18
We investigate the use of prior knowledge of human and animal movement to learn reusable locomotion skills for real legged robots. Our approach builds upon previous work on imitati…