Decision-Making for Satellite Anti-Interception Missions Leveraging Multi-Agent Reinforcement Learning
Many-to-many spacecraft autonomous evasion missions necessitate highly coordinated decision-making among several spacecraft to successfully avoid interceptions. Conventional control methods often struggle to manage the inherent complexity and uncertainty of these missions. In response to this challe...
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| Vydané v: | 2023 5th International Conference on Data-driven Optimization of Complex Systems (DOCS) s. 1 - 7 |
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22.09.2023
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| Abstract | Many-to-many spacecraft autonomous evasion missions necessitate highly coordinated decision-making among several spacecraft to successfully avoid interceptions. Conventional control methods often struggle to manage the inherent complexity and uncertainty of these missions. In response to this challenge, we employ multi-agent reinforcement learning (MARL) algorithms that draw upon machine learning and game theory concepts. Our work is devoted to implementing MARL to facilitate autonomous and intelligent evasion maneuvers by spacecraft, considering the dynamic nature of the space environment and the multiple agent interactions. We utilize the Satellite Tool Kit (STK) as a simulation environment and assess cutting-edge MARL algorithms, with the goal of showcasing the potential of MARL in complex spacecraft evasion missions. Our research endeavors to enhance the autonomy, adaptability, and mission success rate of spacecraft systems under unpredictable circumstances, thereby facilitating more intelligent and adaptive space exploration. |
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| AbstractList | Many-to-many spacecraft autonomous evasion missions necessitate highly coordinated decision-making among several spacecraft to successfully avoid interceptions. Conventional control methods often struggle to manage the inherent complexity and uncertainty of these missions. In response to this challenge, we employ multi-agent reinforcement learning (MARL) algorithms that draw upon machine learning and game theory concepts. Our work is devoted to implementing MARL to facilitate autonomous and intelligent evasion maneuvers by spacecraft, considering the dynamic nature of the space environment and the multiple agent interactions. We utilize the Satellite Tool Kit (STK) as a simulation environment and assess cutting-edge MARL algorithms, with the goal of showcasing the potential of MARL in complex spacecraft evasion missions. Our research endeavors to enhance the autonomy, adaptability, and mission success rate of spacecraft systems under unpredictable circumstances, thereby facilitating more intelligent and adaptive space exploration. |
| Author | Gao, Yang Huo, Jing Yu, Sheng Chen, Zixuan Wang, Dan Wang, Jianqi |
| Author_xml | – sequence: 1 givenname: Zixuan surname: Chen fullname: Chen, Zixuan email: chenzx@nju.edu.cn organization: Nanjing University,Department of Computer Science,Nanjing,China – sequence: 2 givenname: Jianqi surname: Wang fullname: Wang, Jianqi email: 342564222@qq.com organization: Nanjing University,Department of Computer Science,Nanjing,China – sequence: 3 givenname: Dan surname: Wang fullname: Wang, Dan email: wangdan_ict_hit@163.com organization: Institute of Spacecraft System Engineering (ISSE), China Academy of Space Technology (CAST),Beijing,China – sequence: 4 givenname: Sheng surname: Yu fullname: Yu, Sheng email: yusheng86@outlook.com organization: Institute of Spacecraft System Engineering (ISSE), China Academy of Space Technology (CAST),Beijing,China – sequence: 5 givenname: Jing surname: Huo fullname: Huo, Jing email: huojing@nju.edu.cn organization: Nanjing University,Department of Computer Science,Nanjing,China – sequence: 6 givenname: Yang surname: Gao fullname: Gao, Yang email: gaoy@nju.edu.cn organization: Nanjing University,Department of Computer Science,Nanjing,China |
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| Snippet | Many-to-many spacecraft autonomous evasion missions necessitate highly coordinated decision-making among several spacecraft to successfully avoid... |
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| SubjectTerms | Heuristic algorithms Machine learning algorithms multi-agent reinforcement learning Reinforcement learning Satellites Space vehicles spacecraft evasion missions spacecraft systems Training Uncertainty |
| Title | Decision-Making for Satellite Anti-Interception Missions Leveraging Multi-Agent Reinforcement Learning |
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