基于融合任务信息图神经网络的多智能体系统协同规划

戴汉奇; 芦维宁; 李祥隆; 杨君; 孟德山; 刘衍泽; 梁斌

doi:10.1631/FITEE.2100597

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基于融合任务信息图神经网络的多智能体系统协同规划

多智能体系统的体系化和组织化博弈专题 | Updated：2022-09-08

- 基于融合任务信息图神经网络的多智能体系统协同规划
- Cooperative planning of multi-agent systems based on task-oriented knowledge fusion with graph neural networks
- 信息与电子工程前沿（英文） 2022年23卷第7期页码：1069-1076
- Affiliations：
  
  1.Department of Automation, Tsinghua University, Beijing100084, China
  2.Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing100084, China
  3.Office of Science and Technology, Tianjin University, Tianjin300350, China
  4.School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen518107, China
  5.Faculty Office of Electrical and Electronics Engineering, University of Nottingham, Ningbo315154, China
- Author bio：
  
  E-mail: dhq19@mails.tsinghua.edu.cn;
  ‡Corresponding authors
- Funds：
  
  National Natural Science Foundation of China(62073183)
- DOI：10.1631/FITEE.2100597
  中图分类号： TP242.6
- 纸质出版日期：2022-07-23，
  
  收稿日期：2021-12-31，
  
  录用日期：2022-05-23
- Accepted：
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戴汉奇, 芦维宁, 李祥隆, 等. 基于融合任务信息图神经网络的多智能体系统协同规划[J]. 信息与电子工程前沿（英文）, 2022,23(7):1069-1076.

HANQI DAI, WEINING LU, XIANGLONG LI, et al. Cooperative planning of multi-agent systems based on task-oriented knowledge fusion with graph neural networks. [J]. Frontiers of information technology & electronic engineering, 2022, 23(7): 1069-1076.
戴汉奇, 芦维宁, 李祥隆, 等. 基于融合任务信息图神经网络的多智能体系统协同规划[J]. 信息与电子工程前沿（英文）, 2022,23(7):1069-1076. DOI： 10.1631/FITEE.2100597.

HANQI DAI, WEINING LU, XIANGLONG LI, et al. Cooperative planning of multi-agent systems based on task-oriented knowledge fusion with graph neural networks. [J]. Frontiers of information technology & electronic engineering, 2022, 23(7): 1069-1076. DOI： 10.1631/FITEE.2100597.

摘要

协同规划是多智能体系统博弈领域的关键问题之一。本文聚焦每个智能体只有一个局部观测范围和局部通信情况下的协作规划。提出一种新型协同规划框架，该框架将图神经网络与融合任务信息采样方法相结合。本文的两个主要贡献是基于与前期工作的比较：（1）使用图采样与聚合方法（GraphSAGE）实现动态近邻智能体信息融合，这是该方法首次用于处理协同规划问题；（2）提出一种面向任务的采样方法，从特定方向聚合知识，使所提模型获得高效、稳定的训练过程。实验结果证明了所提方法的有效性。

Abstract

Cooperative planning is one of the critical problems in the field of multi-agent system gaming. This work focuses on cooperative planning when each agent has only a local observation range and local communication. We propose a novel cooperative planning architecture that combines a graph neural network with a task-oriented knowledge fusion sampling method. Two main contributions of this paper are based on the comparisons with previous work: (1) we realize feasible and dynamic adjacent information fusion using GraphSAGE (i.e.

Graph SAmple and aggreGatE)

which is the first time this method has been used to deal with the cooperative planning problem

and (2) a task-oriented sampling method is proposed to aggregate the available knowledge from a particular orientation

to obtain an effective and stable training process in our model. Experimental results demonstrate the good performance of our proposed method.

关键词

多智能体系统协同规划图采样与聚合（GraphSAGE）融合任务信息

Keywords

Multi-agent systemCooperative planningGraphSAGETask-oriented knowledge fusion

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