MARL-Based Multi-Satellite Intelligent Task Planning Method

In this article, we propose a solution to multi-satellite intelligent task planning using the multi-agent reinforcement learning (MARL) method. Fristly, we have developed a multi-satellite task planning model based on the Markov game framework. Furthermore, we have computationally designed a satelli...

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Main Authors: Guohui Zhang, Xinhong Li, Gangxuan Hu, Yanyan Li, Xun Wang, Zhibin Zhang
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10332252/
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author Guohui Zhang
Xinhong Li
Gangxuan Hu
Yanyan Li
Xun Wang
Zhibin Zhang
author_facet Guohui Zhang
Xinhong Li
Gangxuan Hu
Yanyan Li
Xun Wang
Zhibin Zhang
author_sort Guohui Zhang
collection DOAJ
description In this article, we propose a solution to multi-satellite intelligent task planning using the multi-agent reinforcement learning (MARL) method. Fristly, we have developed a multi-satellite task planning model based on the Markov game framework. Furthermore, we have computationally designed a satellite state transition function to address the task planning problem and successfully solved it using the multi-agent proximal policy optimization (MAPPO) algorithm. Our experimental results demonstrate that the MARL method exhibits remarkable convergence speed and performance, delivering significant rewards in multi-scale task planning scenarios. Consequently, it proves to be a highly suitable approach for multi-satellite intelligent task planning.
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spelling doaj.art-dadb8ee6de354f30a03de581754606de2024-01-09T00:02:27ZengIEEEIEEE Access2169-35362023-01-011113551713552810.1109/ACCESS.2023.333735810332252MARL-Based Multi-Satellite Intelligent Task Planning MethodGuohui Zhang0https://orcid.org/0000-0002-3782-6696Xinhong Li1Gangxuan Hu2Yanyan Li3Xun Wang4Zhibin Zhang5https://orcid.org/0000-0002-8112-7227Department of Aerospace Science and Technology, Space Engineering University, Beijing, ChinaDepartment of Aerospace Science and Technology, Space Engineering University, Beijing, ChinaDepartment of Aerospace Science and Technology, Space Engineering University, Beijing, ChinaDepartment of Aerospace Science and Technology, Space Engineering University, Beijing, ChinaDepartment of Aerospace Science and Technology, Space Engineering University, Beijing, ChinaDepartment of Aerospace Science and Technology, Space Engineering University, Beijing, ChinaIn this article, we propose a solution to multi-satellite intelligent task planning using the multi-agent reinforcement learning (MARL) method. Fristly, we have developed a multi-satellite task planning model based on the Markov game framework. Furthermore, we have computationally designed a satellite state transition function to address the task planning problem and successfully solved it using the multi-agent proximal policy optimization (MAPPO) algorithm. Our experimental results demonstrate that the MARL method exhibits remarkable convergence speed and performance, delivering significant rewards in multi-scale task planning scenarios. Consequently, it proves to be a highly suitable approach for multi-satellite intelligent task planning.https://ieeexplore.ieee.org/document/10332252/MARLmulti-satellite intelligent task planningMarkov gameMAPPO
spellingShingle Guohui Zhang
Xinhong Li
Gangxuan Hu
Yanyan Li
Xun Wang
Zhibin Zhang
MARL-Based Multi-Satellite Intelligent Task Planning Method
IEEE Access
MARL
multi-satellite intelligent task planning
Markov game
MAPPO
title MARL-Based Multi-Satellite Intelligent Task Planning Method
title_full MARL-Based Multi-Satellite Intelligent Task Planning Method
title_fullStr MARL-Based Multi-Satellite Intelligent Task Planning Method
title_full_unstemmed MARL-Based Multi-Satellite Intelligent Task Planning Method
title_short MARL-Based Multi-Satellite Intelligent Task Planning Method
title_sort marl based multi satellite intelligent task planning method
topic MARL
multi-satellite intelligent task planning
Markov game
MAPPO
url https://ieeexplore.ieee.org/document/10332252/
work_keys_str_mv AT guohuizhang marlbasedmultisatelliteintelligenttaskplanningmethod
AT xinhongli marlbasedmultisatelliteintelligenttaskplanningmethod
AT gangxuanhu marlbasedmultisatelliteintelligenttaskplanningmethod
AT yanyanli marlbasedmultisatelliteintelligenttaskplanningmethod
AT xunwang marlbasedmultisatelliteintelligenttaskplanningmethod
AT zhibinzhang marlbasedmultisatelliteintelligenttaskplanningmethod