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.

本文通过关系强化学习来理解认知系统如何选择在特定任务中有用的特征关系以及如何利用这些表达来有效地与环境交互。我们使用建立在 RRL 中开发的函数逼近器的简单模型来展示我们的方法的潜力，并在需要考虑日益增多的潜在关系的三个 Atari 游戏中进行了训练和测试。在每个游戏中，我们的模型能够选择适当的关系表达式，并逐步建立关系策略。我们探讨了本模型与关系和类比推理模型之间的关系，以及其限制和未来的研究方向。