  Relational Reinforcement Learning (RRL) can offers various desirable
features. Most importantly, it allows for incorporating expert knowledge into
the learning, and hence leading to much faster learning and better
generalization compared to the standard deep reinforcement learning. However,
most of the existing RRL approaches are either incapable of incorporating
expert background knowledge (e.g., in the form of explicit predicate language)
or are not able to learn directly from non-relational data such as image. In
this paper, we propose a novel deep RRL based on a differentiable Inductive
Logic Programming (ILP) that can effectively learn relational information from
image and present the state of the environment as first order logic predicates.
Additionally, it can take the expert background knowledge and incorporate it
into the learning problem using appropriate predicates. The differentiable ILP
allows an end to end optimization of the entire framework for learning the
policy in RRL. We show the efficacy of this novel RRL framework using
environments such as BoxWorld, GridWorld as well as relational reasoning for
the Sort-of-CLEVR dataset.
