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Relational Reinforcement Learning (RRL) can offers various desirablefeatures. Most importantly, it allows for incorporating expert knowledge intothe learning, and hence leading to much faster learning and bettergeneralization compared to the standard deep reinforcement learning. However,most of the existing RRL approaches are either incapable of incorporatingexpert 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. Inthis paper, we propose a novel deep RRL based on a differentiable InductiveLogic Programming (ILP) that can effectively learn relational information fromimage and present the state of the environment as first order logic predicates.Additionally, it can take the expert background knowledge and incorporate itinto the learning problem using appropriate predicates. The differentiable ILPallows an end to end optimization of the entire framework for learning thepolicy in RRL. We show the efficacy of this novel RRL framework usingenvironments such as BoxWorld, GridWorld as well as relational reasoning forthe Sort-of-CLEVR dataset.

Incorporating Relational Background Knowledge into Reinforcement Learning via Differentiable Inductive Logic Programming