基于图注意力网络的异构多智能体系统动态任务分配方法

李中杨; 曹筱可; 蔡奕辰; 孙贵宾; 刘克新

doi:10.12263/DZXB.20251257

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基于图注意力网络的异构多智能体系统动态任务分配方法

信息丰富时代的群体智能理论与技术 | 更新时间：2026-06-16

- 基于图注意力网络的异构多智能体系统动态任务分配方法
- Dynamic Task Allocation Method for Heterogeneous Multi-Agent Systems Based on Graph Attention Networks
- 电子学报 2026年54卷第3期页码：927-937
- 作者机构：
  
  1.北京航空航天大学沈元学院，北京 100191
  2.北京航空航天大学自动化科学与电气工程学院，北京 100191
  3.北京航空航天大学人工智能学院，北京 100191
- 作者简介：
  
  李中杨男，2003年11月出生于浙江省金华市。现为北京航空航天大学沈元学院博士研究生。主要研究方向为多智能体强化学习与机器人智能控制。 E-mail: lizhongyangx@buaa.edu.cn
  曹筱可女，2001年6月出生于陕西省延安市。现为北京航空航天大学自动化科学与电气工程学院硕士研究生。主要研究方向为集群协同任务规划。 E-mail: caoxk@buaa.edu.cn
  蔡奕辰男，2000年4月出生于北京市。现为北京航空航天大学人工智能学院博士研究生。主要研究方向为多智能体系统与集群协同控制。 E-mail: caiyichen@buaa.edu.cn
  孙贵宾男，1995年1月出生于内蒙古自治区乌兰察布市。现为北京航空航天大学自动化科学与电气工程学院副教授。主要研究方向为机器人集群协同控制与决策优化。 E-mail: sunguibinx@buaa.edu.cn
  刘克新男，1988年12月出生于山东省聊城市。现为北京航空航天大学自动化科学与电气工程学院教授。主要研究方向为集群协同制导与控制。 E-mail: kxliu@buaa.edu.cn
- 基金信息：
  
  国家自然科学基金(62503028;62373019);北京市自然科学基金(QY25228)
- DOI：10.12263/DZXB.20251257
  中图分类号： TP18;TP242
- 收稿：2026-01-26，
  
  录用：2026-02-24，
  
  纸质出版：2026-03-25
- 稿件说明：
移动端阅览
李中杨, 曹筱可, 蔡奕辰, 等. 基于图注意力网络的异构多智能体系统动态任务分配方法[J]. 电子学报, 2026, 54(03): 927-937.

LI Zhongyang, CAO Xiaoke, CAI Yichen, et al. Dynamic Task Allocation Method for Heterogeneous Multi-Agent Systems Based on Graph Attention Networks[J]. Acta Electronica Sinica, 2026, 54(03): 927-937.
李中杨, 曹筱可, 蔡奕辰, 等. 基于图注意力网络的异构多智能体系统动态任务分配方法[J]. 电子学报, 2026, 54(03): 927-937. DOI：10.12263/DZXB.20251257

LI Zhongyang, CAO Xiaoke, CAI Yichen, et al. Dynamic Task Allocation Method for Heterogeneous Multi-Agent Systems Based on Graph Attention Networks[J]. Acta Electronica Sinica, 2026, 54(03): 927-937. DOI：10.12263/DZXB.20251257

摘要

异构多智能体系统任务分配问题是多智能体领域的核心问题之一。该问题要求将具有不同能力类型的异构智能体合理分配到需多智能体协作完成的任务中，在实际应用场景中存在的任务新增、智能体失效等动态事件，进一步增加了问题的复杂性。针对现有方法计算代价高昂、难以有效建模异构个体与任务间的复杂依赖关系，以及动态场景自适应决策能力差的问题，本文提出了一种基于图注意力网络的异构多智能体系统动态任务分配方法。该方法引入了动态图构建机制建模异构智能体与任务间的复杂交互关系，并通过节点与边的实时更新实现对动态变化场景的表征。同时本文设计了基于图注意力机制的编解码架构，通过为不同边分配独立的注意力通道解耦异构节点的特征语义，并结合指针式解码器实现了能力与需求的匹配及对变长输入的适应。针对大规模任务分配下的稀疏奖励难题，本文提出了涵盖任务规模与环境动态性双维度的多阶段课程学习策略，通过平滑优化曲面引导策略逐步收敛。仿真实验结果表明，所提方法在动态场景下保持100%的成功率，完成时间较基于学习的对比方法降低了4%～8%，较贪婪算法降低约23%，在大规模场景下仍能保持毫秒级决策速度和高质量的分配结果，验证了方法在动态适应性、规模扩展性和分配方案质量方面的综合优势。

Abstract

The task allocation problem in heterogeneous multi-agent systems is one of the core issues in the multi-agent domain. This problem requires the rational allocation of heterogeneous agents with distinct capabilities to tasks that demand multi-agent collaboration. Moreover

dynamic events in real-world applications

such as the arrival of new tasks and agent failures

further exacerbate the complexity of this problem. To address the limitations of existing methods—such as high computational costs

difficulties in effectively modeling the complex dependencies between heterogeneous agents and tasks

and poor adaptive decision-making capabilities in dynamic scenarios—this paper proposes a dynamic task allocation method for heterogeneous multi-agent systems based on Graph Attention Networks. This method introduces a dynamic graph construction mechanism to model the complex interaction relationships between heterogeneous agents and tasks

explicitly characterizing dynamically evolving scenarios through real-time updates of nodes and edges. Furthermore

an encoder-decoder architecture based on graph attention mechanisms is designed. By assigning independent attention channels to different interaction edges

it decouples the feature semantics of heterogeneous nodes. Combined with a pointer-based decoder

this architecture achieves precise matching between capabilities and requirements

as well as adaptation to variable-length inputs. In addition

to overcome the sparse reward challenge in large-scale task allocation

this paper proposes a multi-stage curriculum learning strategy covering both task scale and environmental dynamics dimensions

which guides the policy to converge progressively by smoothing the optimization landscape. Simulation results demonstrate that the proposed method maintains a 100% allocation success rate across various dynamic scenarios. The task completion time is reduced by 4% to 8% compared to learning-based baselines

and by approximately 23% compared to the greedy algorithm. Even in large-scale scenarios

the method maintains millisecond-level decision-making speeds and yields high-quality allocation results

thereby verifying its comprehensive advantages in dynamic adaptability

scalability

and the quality of allocation schemes.

关键词

Keywords

references

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