AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning

Understand

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 \textit{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 to consider for the purpose of policy adaptation.