Abstract: In recent years, the technique of multi-agent reinforcement learning algorithm has been widely used in the field of artificial intelligence. This paper systematically analyses the multi-agent reinforcement learning algorithm, examines its application and progress in multi-agent systems, and explores the relevant research results in depth. First, it introduces the research background and development history of multi-agent reinforcement learning and summarises the existing relevant research results; second, it briefly reviews the application of traditional reinforcement learning algorithms under different tasks; Then, it highlights the classification of multi-agent reinforcement learning algorithms and their application in multi-agent systems according to the three main types of tasks (path planning, pursuit and escape game, task allocation), challenges, and solutions; finally, it explores the existing algorithm training environments in the field of multi-agents, summarises the improvement of deep learning on multi-agent reinforcement learning algorithms, and looks at the challenges and future research directions in this field. The work in this paper provides a useful reference for researchers to explore this field in depth, helps to further promote the multi-agent reinforcement learning algorithm to achieve more comprehensive development in practical applications, and provides guidance for future research.

Key words: Agent, Reinforcement learning, Multi-agent reinforcement learning, Multi-agent systems

摘要： 近年来，多智能体强化学习算法技术已广泛应用于人工智能领域。本文系统性地分析了多智能体强化学习算法，审视了其在多智能体系统中的应用与进展，并深入调研了相关研究成果。首先，介绍了多智能体强化学习的研究背景和发展历程，并总结了已有的相关研究成果；其次，简要回顾了传统强化学习算法在不同任务下的应用情况；然后，重点强调多智能体强化学习算法分类，并根据三种主要的任务类型（路径规划、追逃博弈、任务分配）对其在多智能体系统中的应用、挑战以及解决方案进行了细致的梳理与分析；最后，调研了多智能体领域中现有的算法训练环境，总结了深度学习对多智能体强化学习算法的改进作用，并展望了该领域所面临的挑战及未来的研究方向。本文的工作为研究人员深入探索这一领域提供了有益参考，有助于进一步推动多智能体强化学习算法在实际应用中实现更全面的发展，并为后续的研究提供了指导。

关键词: 智能体, 强化学习, 多智能体强化学习, 多智能体系统