App. Res. of Comp. Accepted Paper
Vol. 44, 2027 No. 1

Dynamic role discovery and adaptive allocation for multi-agent collaboration

Zheng Yiting
Liu Wei
Huang Meiqin
Zhao Yubo
Sun Xuefeng
School of Computer Science & Engineering, Wuhan Institute of Technology, Wuhan 430205, China
DOI: 10.19734/j.issn.1001-3695.2026.05.0150

Abstract

In multi-agent reinforcement learning (MARL) , complex cooperative tasks often require agents to assume different roles to achieve efficient collaboration. However, existing methods suffer from several limitations, including role discovery relying on manual predefinition, insufficiently adaptive role assignment, and weak correspondence between high-level roles and low-level behaviors. To address these issues, this study proposes a hierarchical role-guided multi-agent proximal policy optimization framework (HR-MAPPO) . The framework explicitly modeled the dynamic interaction topology among agents using a graph attention network (GAT) , performed prior-free and highly discriminative role discovery via momentum contrast (MoCo) learning, and integrated hierarchical decision-making into the MAPPO framework to enable adaptive role assignment and interpretable cooperative execution. Experimental results on six typical cooperative tasks in the StarCraft Ⅱ multi-agent challenge (SMAC) demonstrate that HR-MAPPO achieves optimal or near-optimal test win rates in most scenarios, surpassing MAPPO by approximately 14.3 percentage points on average. The algorithm exhibits faster convergence and superior sample efficiency, particularly in complex cooperative tasks.

Foundation Support

国家自然科学基金面上项目(52371373)
湖北省高等学校优秀中青年科技创新团队计划项目(T2023009)
武汉工程大学第十七届研究生教育创新基金资助项目(CX2025317)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.05.0150
Publish at: Application Research of Computers Accepted Paper, Vol. 44, 2027 No. 1

Publish History

[2026-09-08] Accepted Paper

Cite This Article

郑依婷, 刘玮, 黄美琴, 等. 面向多智能体协作的动态角色发现与自适应分配方法 [J]. 计算机应用研究, 2027, 44 (1). (2026-09-14). https://doi.org/10.19734/j.issn.1001-3695.2026.05.0150. (Zheng Yiting, Liu Wei, Huang Meiqin, et al. Dynamic role discovery and adaptive allocation for multi-agent collaboration [J]. Application Research of Computers, 2027, 44 (1). (2026-09-14). https://doi.org/10.19734/j.issn.1001-3695.2026.05.0150. )

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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