10 citations · 10 across the 1 of their papers we have counts for
2 papers
cs.LG2022★ 10 cited
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…
cs.LG2021
Reinforcement Learning of Implicit and Explicit Control Flow in Instructions
Ethan A. Brooks, Janarthanan Rajendran, Richard L. Lewis +1
Learning to flexibly follow task instructions in dynamic environments poses interesting challenges for reinforcement learning agents. We focus here on the problem of learning contr…