  We propose a simple, practical, and intuitive approach for domain adaptation
in reinforcement learning. Our approach stems from the idea that the agent's
experience in the source domain should look similar to its experience in the
target domain. Building off of a probabilistic view of RL, we formally show
that we can achieve this goal by compensating for the difference in dynamics by
modifying the reward function. This modified reward function is simple to
estimate by learning auxiliary classifiers that distinguish source-domain
transitions from target-domain transitions. Intuitively, the modified reward
function penalizes the agent for visiting states and taking actions in the
source domain which are not possible in the target domain. Said another way,
the agent is penalized for transitions that would indicate that the agent is
interacting with the source domain, rather than the target domain. Our approach
is applicable to domains with continuous states and actions and does not
require learning an explicit model of the dynamics. On discrete and continuous
control tasks, we illustrate the mechanics of our approach and demonstrate its
scalability to high-dimensional tasks.
