(ICCC 2024) Application and Optimization of Multi-Agent Reinforcement Learning in Collaborative Decision-Making

With the rapid development of intelligent systems, Multi-Agent Systems (MAS) have shown unique advantages in solving complex decision-making problems. Particularly in the field of Multi-Agent Reinforcement Learning (MARL), Multiple agents can decompose complex tasks, process information and make decisions in parallel, share experiences, accelerate the learning process, and significantly improve decision quality and efficiency. This paper explores the theoretical underpinnings of MARL and its application to collaborative decision-making, and analyzes practical cases in areas such as transportation system management, automated manufacturing, and smart grids. Additionally, it addresses challenges in strategy coordination, handling dynamic environments, and improving learning efficiency. This paper proposes several optimization strategies and introduces reservoir group optimization experiments. By comparing with single-agent algorithms, it verifies that multi-agent systems can coordinate multiple reservoirs, enhance convergence speed, and achieve higher power generation efficiency, demonstrating better practical application prospects. Furthermore, the future trends of MARL, including technological advancements, potential applications, and challenges, are discussed.