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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Bibliographic Details
Published inIEEE access Vol. 11; pp. 135517 - 135528
Main Authors Zhang, Guohui, Li, Xinhong, Hu, Gangxuan, Li, Yanyan, Wang, Xun, Zhang, Zhibin
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Games
MAPPO
Markov game
Markov processes
MARL
multi-satellite intelligent task planning
Multiagent systems
Planning
Predictive models
Reinforcement learning
Satellites
Task analysis
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3337358