A SYSTEMATIC REVIEW OF DISTRIBUTED REINFORCEMENT LEARNING FOR BUILDING-TO-DISTRICT ENERGY NETWORKS

Abstract: Distributed Reinforcement Learning (RL) has emerged as a promising approach for coordination HVAC control across buildings in district energy systems. However, existing studies vary widely in system architecture, problem focus and practical feasibility. This paper presents a structured review of recent work on distributed RL for Building Energy System (BES). We introduce a framework that classifies approaches according to the level of distribution and the key problem dimensions addressed, which cover scalability, coordination, privacy, safety and training efficiency. Using a research map, we analyze trends, highlight unresolved challenges, and identify directions toward practically deployable, privacy-aware, and safe distributed HVAC control.