🤖 AI Summary
Reinforcement learning (RL) faces significant bottlenecks in generalization, sample efficiency, safety, and interpretability—largely due to overreliance on low-level representation learning and insufficient integration of high-level declarative domain knowledge (e.g., facts, rules, and relational constraints). This paper presents the first systematic survey of knowledge representation and reasoning (KRR)-enhanced RL. We propose a novel logic-driven KRR-RL integration paradigm grounded in logic programming, first-order logic formalisms, differentiable logical inference, and symbolic–subsymbolic hybrid modeling. Our framework enables high-order symbolic knowledge to guide policy learning and value estimation. We categorize six core technical approaches, identify critical challenges—including knowledge grounding, scalability, and credit assignment—and outline promising future directions such as verifiable logical reasoning and dynamic knowledge acquisition.
📝 Abstract
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.