17 citations · 30 across the 4 of their papers we have counts for
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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.LG2022★ 3 cited
Large Language Models can Implement Policy Iteration
Ethan Brooks, Logan Walls, Richard L. Lewis +1
This work presents In-Context Policy Iteration, an algorithm for performing Reinforcement Learning (RL), in-context, using foundation models. While the application of foundation mo…