  Despite numerous successes in Deep Reinforcement Learning (DRL), the learned
policies are not interpretable. Moreover, since DRL does not exploit symbolic
relational representations, it has difficulties in coping with structural
changes in its environment (such as increasing the number of objects).
Relational Reinforcement Learning, on the other hand, inherits the relational
representations from symbolic planning to learn reusable policies. However, it
has so far been unable to scale up and exploit the power of deep neural
networks. We propose Deep Explainable Relational Reinforcement Learning
(DERRL), a framework that exploits the best of both -- neural and symbolic
worlds. By resorting to a neuro-symbolic approach, DERRL combines relational
representations and constraints from symbolic planning with deep learning to
extract interpretable policies. These policies are in the form of logical rules
that explain how each decision (or action) is arrived at. Through several
experiments, in setups like the Countdown Game, Blocks World, Gridworld, and
Traffic, we show that the policies learned by DERRL can be applied to different
configurations and contexts, hence generalizing to environmental modifications.
