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Cooperative planning of multi-agent systems based on task-oriented knowledge fusion with graph neural networks

Author(s): Hanqi DAI, Weining LU, Xianglong LI, Jun YANG, Deshan MENG, Yanze LIU, Bin LIANG
Affiliation(s): Department of Automation, Tsinghua University, Beijing 100084, China; more
Corresponding email(s): dhq19@mails.tsinghua.edu.cn, luwn@tsinghua.edu.cn
Key Words: Multi-agent system; Cooperative planning; GraphSAGE; Task-oriented knowledge fusion

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Hanqi DAI, Weining LU, Xianglong LI, Jun YANG, Deshan MENG, Yanze LIU, Bin LIANG. Cooperative planning of multi-agent systems based on task-oriented knowledge fusion with graph neural networks[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2100597

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author="Hanqi DAI, Weining LU, Xianglong LI, Jun YANG, Deshan MENG, Yanze LIU, Bin LIANG",
journal="Frontiers of Information Technology & Electronic Engineering",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.2100597"
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%T Cooperative planning of multi-agent systems based on task-oriented knowledge fusion with graph neural networks
%A Hanqi DAI
%A Weining LU
%A Xianglong LI
%A Jun YANG
%A Deshan MENG
%A Yanze LIU
%A Bin LIANG
%J Frontiers of Information Technology & Electronic Engineering
%P 1069-1076
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%I Zhejiang University Press & Springer
doi="https://doi.org/10.1631/FITEE.2100597"

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T1 - Cooperative planning of multi-agent systems based on task-oriented knowledge fusion with graph neural networks
A1 - Hanqi DAI
A1 - Weining LU
A1 - Xianglong LI
A1 - Jun YANG
A1 - Deshan MENG
A1 - Yanze LIU
A1 - Bin LIANG
J0 - Frontiers of Information Technology & Electronic Engineering
SP - 1069
EP - 1076
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Y1 - in press
PB - Zhejiang University Press & Springer
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doi="https://doi.org/10.1631/FITEE.2100597"

Abstract
Chinese Summary
Academic Network
Reviewer Comment

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.

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^1,2，芦维宁²，李祥隆³，杨君¹，孟德山⁴，刘衍泽⁵，梁斌¹
¹清华大学自动化系，中国北京市，100084
²清华大学北京信息科学与技术国家研究中心，中国北京市，100084
³天津大学科学技术发展研究院，中国天津市，300350
⁴中山大学航空航天学院，中国深圳市，518107
⁵诺丁汉大学电气与电子工程系，中国宁波市，315154
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。

关键词组：多智能体系统；协同规划；图采样与聚合（GraphSAGE）；融合任务信息

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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

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