##基于多智能体强化学习的系统优化

Abstract

This paper investigates the application of multi-agent reinforcement learning (MARL) for optimizing complex systems. Traditional system optimization approaches often rely on centralized control or distributed control strategies, which can struggle with the inherent complexity and dynamic nature of many real-world systems. This research proposes a novel framework utilizing a population of intelligent agents trained through MARL to autonomously optimize these systems. The core concept involves decomposing the complex system into multiple agents, each responsible for optimizing a specific sub-objective. These agents interact through a combination of cooperation and competition, ultimately leading to overall system optimization. We explore the theoretical foundations of this approach, outlining the key components and the learning dynamics involved. The paper demonstrates the potential of MARL to overcome the limitations of conventional methods, offering a more adaptive and robust solution for complex system optimization problems. The effectiveness of this approach is discussed through a theoretical analysis and a conceptual design, paving the way for future research and practical implementations. ---