Reinforcement Learning With Knowledge Representation And Reasoning: A Brief Survey

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 endea

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyyu2023reinforcement

Related papers

• Foundations For Transfer In Reinforcement Learning: A Taxonomy Of Knowledge Modalities (2023)0.00
• Learning Symbolic Representations For Reinforcement Learning Of Non-markovian Behavior (2023)0.00
• A Survey On Explainable Reinforcement Learning: Concepts, Algorithms, Challenges (2022)0.00
• A Comprehensive Survey Of Reinforcement Learning: From Algorithms To Practical Challenges (2024)0.00
• Causal Reinforcement Learning: A Survey (2023)0.00
• Statistical Reinforcement Learning In The Real World: A Survey Of Challenges And Future Directions (2026)0.00
• A Survey Of State Representation Learning For Deep Reinforcement Learning (2025)0.00
• State Representation Learning For Control: An Overview (2018)17.39