Physical Sciences and Mathematics Commons^™

Adarl: What, Where, And How To Adapt In Transfer Reinforcement Learning, Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang

Machine Learning Faculty Publications

One practical challenge in reinforcement learning (RL) is how to make quick adaptations when faced with new environments. In this paper, we propose a principled framework for adaptive RL, called AdaRL, that adapts reliably and efficiently to changes across domains with a few samples from the target domain, even in partially observable environments. Specifically, we leverage a parsimonious graphical representation that characterizes structural relationships over variables in the RL system. Such graphical representations provide a compact way to encode what and where the changes across domains are, and furthermore inform us with a minimal set of changes that one has …