In-context Reinforcement Learning with Algorithm Distillation
Michael Laskin,Luyu Wang,Junhyuk Oh,Emilio Parisotto,Stephen Spencer,Richie Steigerwald,DJ Strouse,Steven Stenberg Hansen,Angelos Filos,Ethan Brooks,maxime gazeau,Himanshu Sahni,Satinder Singh,Volodymyr Mnih
We propose Algorithm Distillation (AD), a method for distilling reinforcement learning (RL) algorithms into neural networks by modeling their training histories with a causal sequence model. Algorithm Distillation treats learning to reinforcement learn as an across-episode sequential prediction problem. A dataset of learning histories is generated by a source RL algorithm, and then a causal transformer is trained by autoregressively predicting actions given their preceding learning histories as context. Unlike sequential policy prediction architectures that distill post-learning or expert sequences, AD is able to improve its policy entirely in-context without updating its network parameters. We demonstrate that AD can reinforcement learn in-context in a variety of environments with sparse rewards, combinatorial task structure, and pixel-based observations, and find that AD learns a more data-efficient RL algorithm than the one that generated the source data.
