Cargando…

Graph MADDPG with RNN for multiagent cooperative environment

Multiagent systems face numerous challenges due to environmental uncertainty, with scalability being a critical issue. To address this, we propose a novel multi-agent cooperative model based on a graph attention network. Our approach considers the relationship between agents and continuous action sp...

Descripción completa

Detalles Bibliográficos
Autores principales: Wei, Xiaolong, Cui, WenPeng, Huang, Xianglin, Yang, LiFang, Tao, Zhulin, Wang, Bing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10339649/
https://www.ncbi.nlm.nih.gov/pubmed/37457642
http://dx.doi.org/10.3389/fnbot.2023.1185169
_version_ 1785071893842231296
author Wei, Xiaolong
Cui, WenPeng
Huang, Xianglin
Yang, LiFang
Tao, Zhulin
Wang, Bing
author_facet Wei, Xiaolong
Cui, WenPeng
Huang, Xianglin
Yang, LiFang
Tao, Zhulin
Wang, Bing
author_sort Wei, Xiaolong
collection PubMed
description Multiagent systems face numerous challenges due to environmental uncertainty, with scalability being a critical issue. To address this, we propose a novel multi-agent cooperative model based on a graph attention network. Our approach considers the relationship between agents and continuous action spaces, utilizing graph convolution and recurrent neural networks to define these relationships. Graph convolution is used to define the relationship between agents, while recurrent neural networks define continuous action spaces. We optimize and model the multiagent system by encoding the interaction weights among agents using the graph neural network and the weights between continuous action spaces using the recurrent neural network. We evaluate the performance of our proposed model by conducting experimental simulations using a 3D wargame engine that involves several unmanned air vehicles (UAVs) acting as attackers and radar stations acting as defenders, where both sides have the ability to detect each other. The results demonstrate that our proposed model outperforms the current state-of-the-art methods in terms of scalability, robustness, and learning efficiency.
format Online
Article
Text
id pubmed-10339649
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-103396492023-07-14 Graph MADDPG with RNN for multiagent cooperative environment Wei, Xiaolong Cui, WenPeng Huang, Xianglin Yang, LiFang Tao, Zhulin Wang, Bing Front Neurorobot Neuroscience Multiagent systems face numerous challenges due to environmental uncertainty, with scalability being a critical issue. To address this, we propose a novel multi-agent cooperative model based on a graph attention network. Our approach considers the relationship between agents and continuous action spaces, utilizing graph convolution and recurrent neural networks to define these relationships. Graph convolution is used to define the relationship between agents, while recurrent neural networks define continuous action spaces. We optimize and model the multiagent system by encoding the interaction weights among agents using the graph neural network and the weights between continuous action spaces using the recurrent neural network. We evaluate the performance of our proposed model by conducting experimental simulations using a 3D wargame engine that involves several unmanned air vehicles (UAVs) acting as attackers and radar stations acting as defenders, where both sides have the ability to detect each other. The results demonstrate that our proposed model outperforms the current state-of-the-art methods in terms of scalability, robustness, and learning efficiency. Frontiers Media S.A. 2023-06-29 /pmc/articles/PMC10339649/ /pubmed/37457642 http://dx.doi.org/10.3389/fnbot.2023.1185169 Text en Copyright © 2023 Wei, Cui, Huang, Yang, Tao and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Wei, Xiaolong
Cui, WenPeng
Huang, Xianglin
Yang, LiFang
Tao, Zhulin
Wang, Bing
Graph MADDPG with RNN for multiagent cooperative environment
title Graph MADDPG with RNN for multiagent cooperative environment
title_full Graph MADDPG with RNN for multiagent cooperative environment
title_fullStr Graph MADDPG with RNN for multiagent cooperative environment
title_full_unstemmed Graph MADDPG with RNN for multiagent cooperative environment
title_short Graph MADDPG with RNN for multiagent cooperative environment
title_sort graph maddpg with rnn for multiagent cooperative environment
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10339649/
https://www.ncbi.nlm.nih.gov/pubmed/37457642
http://dx.doi.org/10.3389/fnbot.2023.1185169
work_keys_str_mv AT weixiaolong graphmaddpgwithrnnformultiagentcooperativeenvironment
AT cuiwenpeng graphmaddpgwithrnnformultiagentcooperativeenvironment
AT huangxianglin graphmaddpgwithrnnformultiagentcooperativeenvironment
AT yanglifang graphmaddpgwithrnnformultiagentcooperativeenvironment
AT taozhulin graphmaddpgwithrnnformultiagentcooperativeenvironment
AT wangbing graphmaddpgwithrnnformultiagentcooperativeenvironment