A survey on physics informed reinforcement learning: Review and open problems

• Survey works on physics information fusion in reinforcement learning. • Classify works on physics type, fusion method and learning pipeline. • Analyse trends in algorithm, physics, architecture, and application selection. • Discuss reinforcement learning challenges addressed. • Present insights, limitations, and future research directions. The fusion of physical information in machine learning frameworks has revolutionized many application areas. This involves enhancing the learning process by incorporating physical constraints and adhering to physical laws. This work explores their utility for reinforcement learning applications. A thorough review of the literature on the fusion of physics information or physics priors in reinforcement learning approaches, commonly referred to as physics-informed reinforcement learning (PIRL), is presented. A novel taxonomy is introduced with the reinforcement learning pipeline as the backbone to classify existing works, compare and contrast them, and derive crucial insights. Existing works are analyzed with regard to the representation/form of the governing physics modeled for integration, their specific contribution to the typical reinforcement learning architecture, and their connection to the underlying reinforcement learning pipeline stages. Core learning architectures and physics incorporation biases (i.e., observational, inductive, and learning) of existing PIRL approaches are identified and used to further categorize the works for better understanding and adaptation. By providing a comprehensive perspective on the implementation of the physics-informed capability, the taxonomy presents a cohesive approach to PIRL. It identifies the areas where this approach has been applied, as well as the gaps and opportunities that exist. Additionally, the review highlights unresolved issues and challenges, while also incorporating potential and emerging solutions to guide future research. This nascent field holds great potential for enhancing reinforcement learning algorithms by increasing their physical plausibility, precision, data efficiency, and applicability in real-world scenarios.