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.

翻译：强化学习近年来取得了巨大的进展，但由于存在系统泛化能力差、样本效率低以及安全性与可解释性不足等问题，其在解决复杂现实问题中仍面临重大障碍。此类困境的核心原因可归结为：大多数工作仅关注使用表征模型描述奖励、状态和动作等原子组件以计算价值函数或策略的计算层面，从而忽略了可通过先验提供或随时间推移通过推理获取的丰富高阶声明性领域知识（如事实、关系和规则）。近年来，利用知识表示与推理（通常采用逻辑语言）来实现强化学习中更高层次的抽象表示和高效学习的研究兴趣迅速增长。本综述初步概述了这些利用KRR优势解决强化学习中各种问题的尝试，并讨论了该领域当前面临的挑战性开放问题及未来可能的研究方向。