In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
See More
Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 5
1356-1361

Gradient-based multi-agent meta deep reinforcement learning algorithm

Zhao Chunyu
Lai Jun
Chen Xiliang
Zhang Renwen
College of Command & Control Engineering, Army Engineering University of PLA, Nanjing 210007, China
DOI: 10.19734/j.issn.1001-3695.2023.09.0411

Abstract

Multi-agent systems have a wide range of applications in many fields, such as autonomous driving, intelligent logistics, and medical collaboration, etc. However, due to technological advances and increased system requirements, these systems face challenges such as large scale and high complexity, and often suffer from inefficient training and poor adaptability. To address these problems, this paper proposed a multi-agent first-order meta proximal policy optimization(MAMPPO) method by extending gradient-based meta-learning to multi-agent deep reinforcement learning. The method learned the initial model parameters in the multi-agent system to provide a new perspective for improving the performance of multi-agent deep reinforcement learning. It made full use of the previous experience in the process of multi-agent reinforcement learning to find the most sensitive parameters in the direction of gradient descent through repeated adaptation, and learned the initial parameters so that the model training starts from the optimal starting point. This method effectively improved the decision-making efficiency of the joint policy, and led to a significant increase in the speed of its policy change, which significantly accelerated the speed of adaptation in the face of a new situation. Experimental results on StarCraft Ⅱ show that the MAMPPO method can significantly improve the training speed and adaptability, which provides a new solution for the subsequent improvement of the training efficiency and adaptability of multi-agent reinforcement learning.

Foundation Support

国家自然科学基金资助项目(61806221)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.09.0411
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 5
Section: Algorithm Research & Explore
Pages: 1356-1361
Serial Number: 1001-3695(2024)05-011-1356-06

Publish History

[2023-11-21] Accepted Paper
[2024-05-05] Printed Article

Cite This Article

赵春宇, 赖俊, 陈希亮, 等. 一种基于梯度的多智能体元深度强化学习算法 [J]. 计算机应用研究, 2024, 41 (5): 1356-1361. (Zhao Chunyu, Lai Jun, Chen Xiliang, et al. Gradient-based multi-agent meta deep reinforcement learning algorithm [J]. Application Research of Computers, 2024, 41 (5): 1356-1361. )

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.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)