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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 10
2957-2961

Multi-agent reinforcement learning based on observation relation extraction

Xu Shuqing1,2,3,4
Zang Chuanzhi5
Wang Xin1,2,3
Liu Ding1,2,3,4
Liu Yuqi1,2,3
Zeng Peng1,2,3
1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
3. Innovation Institute of Robotics & Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
4. University of Chinese Academy of Sciences, Beijing 100049, China
5. Shenyang University of Technology, Shenyang 110023, China
DOI: 10.19734/j.issn.1001-3695.2022.03.0138

Abstract

In order to overcome the challenges of policy learning in MAS, such as the unstable environment and the interaction of agent decisions, this paper proposed a method named ORE, which used a complete graph to model the relationship between different parts of each agent's observation, and took advantage of the attention mechanism to calculate the importance of the relationship between different parts of each agent's observation. By applying the above method to multi-agent reinforcement learning algorithms based on value decomposition, this paper proposed multi-agent reinforcement learning algorithms based on observation relation extraction. Experimental results on SMAC show the proposed algorithms with ORE leads to better performance than the original algorithms in terms of both convergence speed and final performance.

Foundation Support

国家自然科学基金资助项目(92067205)
辽宁省自然科学基金资助项目(2020-KF-11-02)
机器人学国家重点实验室开放课题(2020-Z11)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.03.0138
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 10
Section: Algorithm Research & Explore
Pages: 2957-2961
Serial Number: 1001-3695(2022)10-010-2957-05

Publish History

[2022-06-08] Accepted Paper
[2022-10-05] Printed Article

Cite This Article

许书卿, 臧传治, 王鑫, 等. 基于观测空间关系提取的多智能体强化学习 [J]. 计算机应用研究, 2022, 39 (10): 2957-2961. (Xu Shuqing, Zang Chuanzhi, Wang Xin, et al. Multi-agent reinforcement learning based on observation relation extraction [J]. Application Research of Computers, 2022, 39 (10): 2957-2961. )

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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