  Reinforcement Learning (RL) provides a framework in which agents can be
trained, via trial and error, to solve complex decision-making problems.
Learning with little supervision causes RL methods to require large amounts of
data, which renders them too expensive for many applications (e.g. robotics).
By reusing knowledge from a different task, knowledge transfer methods present
an alternative to reduce the training time in RL. Given how severe data
scarcity can be, there has been a growing interest for methods capable of
transferring knowledge across different domains (i.e. problems with different
representation) due to the flexibility they offer. This review presents a
unifying analysis of methods focused on transferring knowledge across different
domains. Through a taxonomy based on a transfer-approach categorization, and a
characterization of works based on their data-assumption requirements, the
objectives of this article are to 1) provide a comprehensive and systematic
revision of knowledge transfer methods for the cross-domain RL setting, 2)
categorize and characterize these methods to provide an analysis based on
relevant features such as their transfer approach and data requirements, and 3)
discuss the main challenges regarding cross-domain knowledge transfer, as well
as ideas of future directions worth exploring to address these problems.
