  Humans perceive the world in terms of objects and relations between them. In
fact, for any given pair of objects, there is a myriad of relations that apply
to them. How does the cognitive system learn which relations are useful to
characterize the task at hand? And how can it use these representations to
build a relational policy to interact effectively with the environment? In this
paper we propose that this problem can be understood through the lens of a
sub-field of symbolic machine learning called relational reinforcement learning
(RRL). To demonstrate the potential of our approach, we build a simple model of
relational policy learning based on a function approximator developed in RRL.
We trained and tested our model in three Atari games that required to consider
an increasingly number of potential relations: Breakout, Pong and Demon Attack.
In each game, our model was able to select adequate relational representations
and build a relational policy incrementally. We discuss the relationship
between our model with models of relational and analogical reasoning, as well
as its limitations and future directions of research.
