  Reinforcement Learning(RL) has achieved tremendous development in recent
years, but still faces significant obstacles in addressing complex real-life
problems due to the issues of poor system generalization, low sample efficiency
as well as safety and interpretability concerns. The core reason underlying
such dilemmas can be attributed to the fact that most of the work has focused
on the computational aspect of value functions or policies using a
representational model to describe atomic components of rewards, states and
actions etc, thus neglecting the rich high-level declarative domain knowledge
of facts, relations and rules that can be either provided a priori or acquired
through reasoning over time. Recently, there has been a rapidly growing
interest in the use of Knowledge Representation and Reasoning(KRR) methods,
usually using logical languages, to enable more abstract representation and
efficient learning in RL. In this survey, we provide a preliminary overview on
these endeavors that leverage the strengths of KRR to help solving various
problems in RL, and discuss the challenging open problems and possible
directions for future work in this area.
