##基于多智能体强化学习的分布式资源管理

Abstract

This paper investigates the application of Multi-Agent Reinforcement Learning (MARL) to the problem of distributed resource management. Traditional approaches to this problem often rely on centralized control, which can be inefficient and vulnerable to single points of failure. This research proposes a decentralized solution utilizing MARL, where multiple agents, each responsible for managing a subset of resources, learn to coordinate and optimize overall system performance through interaction and reward signals. We formulate the resource management task as a multi-agent Markov Decision Process (MAMP), leveraging algorithms such as Independent Q-Learning (IQL) and Centralized Training with Decentralized Execution (CTDE) to train the agents. The core claim is that MARL offers a viable framework for distributed resource management. The mechanism relies on the agents learning optimal strategies through trial and error, adapting to changing conditions, and ultimately achieving greater efficiency and robustness compared to traditional methods. The novelty of this work lies in its exploration of MARL's potential in this domain, a field still in its nascent stages. The results demonstrate the feasibility and effectiveness of this approach.