Recent Advances in Multi-Agent Reinforcement Learning for Intelligent Automation and Control of Water Environment Systems
Lei Jia, Yan Pei
Abstract
Multi-agent reinforcement learning (MARL) has demonstrated significant application potential in addressing cooperative control, policy optimization, and task allocation problems in complex systems. This paper focuses on its applications and development in water environmental systems, providing a systematic review of the theoretical foundations of multi-agent systems and reinforcement learning and summarizing three representative categories of mainstream MARL algorithms. Typical control scenarios in water systems are also examined. From the perspective of cooperative control, this paper investigates the modeling mechanisms and policy coordination strategies of MARL in key tasks such as water supply scheduling, hydro-energy co-regulation, and autonomous monitoring. It further analyzes the challenges and solutions for improving global cooperative efficiency under practical constraints such as limited resources, system heterogeneity, and unstable communication. Additionally, recent progress in cross-domain generalization, integrated communication–perception frameworks, and system-level robustness enhancement is summarized. This work aims to provide a theoretical foundation and key insights for advancing research and practical applications of MARL-based intelligent control in water infrastructure systems.