Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 9
2743-2748,2754

Research on multi-agent hierarchical reinforcement learning skill discovery method based on weighted value function decomposition

Zou Qijie1
Li Wenxue1
Gao Bing1
Zhao Xiling1
Zhang Rubo2
1. Dept. of Information Engineering, Dalian University, Dalian Liaoning 116622, China
2. Dept. of Mechanical & Electrical Engineering, Dalian Nationalities University, Dalian Liaoning 116600, China
DOI: 10.19734/j.issn.1001-3695.2022.12.0795

Abstract

Aiming at the problem of dimension explosion and sparse rewards caused by the increase in the number of agents and the dynamic instability of the environment in most multi-agent reinforcement learning algorithms, this paper proposed a multi-agent hierarchical reinforcement learning skill discovery algorithm based on weighted value function decomposition. Firstly, the algorithm combined the architecture of centralized training and decentralized execution with hierarchical reinforcement learning, and adopted the method of weighted value function decomposition in the upper level to solve the problem that agents tended to ignore the optimal strategy and chose the suboptimal strategy in the training process. Secondly, it adopted the independent Q learning algorithm in the lower level to enable it to deal with high-dimensional complex tasks in a multi-agent environment in a decentralized manner. Finally, it introduced a skill discovery strategy on the basis of independent Q learning at the lower level, so that agents could learn complementary skills from each other. Compared the algorithm with the multi-agent reinforcement learning algorithms and the hierarchical reinforcement learning algorithms on the two simulation experimental platforms of simple team movement and StarCraft Ⅱ respectively. The experiment shows that the algorithm has improved performance indicators such as rewards and the victory rate of both sides, improves the decision-making ability and convergence speed of the entire multi-agent system, and verifies the feasibility of the algorithm.

Foundation Support

国家自然科学基金资助项目(61673084)
2021年辽宁省教育厅项目(LJKZ1180)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.12.0795
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 9
Section: Algorithm Research & Explore
Pages: 2743-2748,2754
Serial Number: 1001-3695(2023)09-027-2743-06

Publish History

[2023-03-03] Accepted Paper
[2023-09-05] Printed Article

Cite This Article

邹启杰, 李文雪, 高兵, 等. 基于加权值函数分解的多智能体分层强化学习技能发现方法 [J]. 计算机应用研究, 2023, 40 (9): 2743-2748,2754. (Zou Qijie, Li Wenxue, Gao Bing, et al. Research on multi-agent hierarchical reinforcement learning skill discovery method based on weighted value function decomposition [J]. Application Research of Computers, 2023, 40 (9): 2743-2748,2754. )

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.

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