##基于多智能体强化学习的分布式控制系统设计

Abstract

This paper proposes a novel approach to designing distributed control systems utilizing Multi-Agent Reinforcement Learning (MARL). Traditional distributed control systems often rely on manually engineered control strategies, leading to limitations in adaptability, robustness, and scalability. This work introduces a framework where a collection of agents learn optimal control policies autonomously through MARL. The system is structured as a multi-agent environment, with each agent tasked with learning its own control strategy. The core of the system relies on MARL algorithms that enable both cooperative and competitive interactions among the agents, ultimately leading to a distributed system capable of adapting to dynamic environments and achieving optimal performance. The presented methodology offers a promising alternative to conventional distributed control methods, promising enhanced resilience and performance in complex, uncertain, and evolving operational scenarios. The system aims to address the inherent challenges of centralized control, particularly in large-scale systems, by distributing intelligence and allowing for emergent behaviors.