Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (21): 1-13.DOI: 10.3778/j.issn.1002-8331.2104-0432

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Overview on Reinforcement Learning of Multi-agent Game

WANG Jun, CAO Lei, CHEN Xiliang, LAI Jun, ZHANG Legui   

  1. College of Command and Control Engineering, Army Engineering University of PLA, Nanjing 210007, China
  • Online:2021-11-01 Published:2021-11-04



  1. 陆军工程大学 指挥控制工程学院,南京 210007


The use of deep reinforcement learning to solve single-agent tasks has made breakthrough progress. Since the complexity of multi-agent systems, common algorithms cannot solve the main difficulties. At the same time, due to the increase in the number of agents, taking the expected value of maximizing the cumulative return of a single agent as the learning goal often fails to converge and some special convergence points do not satisfy the rationality of the strategy. For practical problems that there is no optimal solution, the reinforcement learning algorithm is even more helpless. The introduction of game theory into reinforcement learning can solve the interrelationship of agents very well and explain the rationality of the strategy corresponding to the convergence point. More importantly, it can use the equilibrium solution to replace the optimal solution in order to obtain a relatively effective strategy. Therefore, this article investigates the reinforcement learning algorithms that have emerged in recent years from the perspective of game theory, summarizes the important and difficult points of current game reinforcement learning algorithms and gives several breakthrough directions that may solve the above-mentioned difficulties.

Key words: multi-agent, reinforcement learning, game theory



关键词: 多智能体, 强化学习, 博弈论