  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. We show that by explicitly
leveraging this compact representation to encode changes, we can efficiently
adapt the policy to the target domain, in which only a few samples are needed
and further policy optimization is avoided. We illustrate the efficacy of AdaRL
through a series of experiments that vary factors in the observation,
transition, and reward functions for Cartpole and Atari games.
