Logic + Reinforcement Learning + Deep Learning: A Survey

Abstract

Reinforcement learning has made significant strides in recent years, including in the development of Atari and Go-playing agents. It is now widely acknowledged that logical syntax adds considerable flexibility in both the modelling of domains as well as the interpretability of domains. In this survey paper, we cover the fundamentals of how logic, reinforcement learning, and deep learning can be unified, with some ideas for future work.