  One approach to explaining the hierarchical levels of understanding within a
machine learning model is the symbolic method of inductive logic programming
(ILP), which is data efficient and capable of learning first-order logic rules
that can entail data behaviour. A differentiable extension to ILP, so-called
differentiable Neural Logic (dNL) networks, are able to learn Boolean functions
as their neural architecture includes symbolic reasoning. We propose an
application of dNL in the field of Relational Reinforcement Learning (RRL) to
address dynamic continuous environments. This represents an extension of
previous work in applying dNL-based ILP in RRL settings, as our proposed model
updates the architecture to enable it to solve problems in continuous RL
environments. The goal of this research is to improve upon current ILP methods
for use in RRL by incorporating non-linear continuous predicates, allowing RRL
agents to reason and make decisions in dynamic and continuous environments.

Machine Learning;Logic in Computer Science;Symbolic Computation