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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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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 |
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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 |
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