Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (5): 13-24.DOI: 10.3778/j.issn.1002-8331.1912-0100

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Overview of Multi-Agent Deep Reinforcement Learning

SUN Yu, CAO Lei, CHEN Xiliang, XU Zhixiong, LAI Jun   

  1. 1.College of Command and Control Engineering, Army Engineering University of PLA, Nanjing 210007, China
    2.Unit 31102 of PLA, China
  • Online:2020-03-01 Published:2020-03-06



  1. 1.陆军工程大学 指挥控制工程学院,南京 210007


Multi-agent deep reinforcement learning is an emerging research hotspot and application direction in the field of machine learning and artificial intelligence. It covers many algorithms, rules, and frameworks, and is widely used in autonomous driving, energy allocation, formation control, trajectory planning,routing planning and social dilemma, it has extremely high research value and significance. The paper first briefly introduces the basic theory and development history of multi-agent deep reinforcement learning, then elaborates the existing classic algorithms according to four classification:non-association type, communication rule based type, mutual cooperation type and modeling learning type, then summarizes the practical application of multi-agent deep reinforcement learning and briefly lists the existing test platforms. The paper finally summarizes the challenges and future directions in theory, algorithms and applications of multi-agent deep reinforcement learning.

Key words: reinforcement learning, deep learning, multi-agent system, multi-agent deep reinforcement learning



关键词: 强化学习, 深度学习, 多智能体系统, 多智能体深度强化学习