Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 3
802-806

Embedding rules into multiagent reinforcement learning based on iterative training

Li Yuan
Xu Xinhai
Academy of Military Sciences, Beijing 100190, China
DOI: 10.19734/j.issn.1001-3695.2021.09.0351

Abstract

Multi-agent reinforcement learning methods have been made great progress in simulation, game, recommendation systems and so on. However, the complex problems in the real word bring great difficulties for reinforcement learning, such as many useless explorations, slow converging speed and poor performance of the learning. This paper studied the problem of multi-agent reinforcement learning with embedded rules and proposed a method to combine rules and the learning method based on an iterative training mechanism. This method designed a multi-agent reinforcement learning method with embedded rules, and a rule selection model. This paper introduced an iterative training mechanism to combine the two methods together. The proposed method could decide whether to use the result of a reinforcement learning or the result of a rule based on the real-time game state. It could effectively solve the problem that which rule should be selected and when it would be used. Finally, it made an experiment on a multi-agent combat platform which was published by the China Electronics Technology Group. By fighting with the built-in opponent in the platform, it found that the method with rules could achieve 60% win rate with 14 thousand rounds while achieve 50% win rate with 17 thousand rounds for the method without rules. The results show that the proposed method can effectively improve the converging speed and the performance of multi-agent reinforcement learning.

Foundation Support

国家青年科学基金资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.09.0351
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 3
Section: Algorithm Research & Explore
Pages: 802-806
Serial Number: 1001-3695(2022)03-027-0802-05

Publish History

[2021-11-16] Accepted Paper
[2022-03-05] Printed Article

Cite This Article

李渊, 徐新海. 基于组合训练的规则嵌入多智能体强化学习方法 [J]. 计算机应用研究, 2022, 39 (3): 802-806. (Li Yuan, Xu Xinhai. Embedding rules into multiagent reinforcement learning based on iterative training [J]. Application Research of Computers, 2022, 39 (3): 802-806. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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