A survey of deep reinforcement learning technologies for intelligent air combat
Major aviation nations and related research institutions are focusing on exploration and research of key technologies for intelligent air combat. Deep reinforcement learning combines the perceptual ability of deep learning with the decision-making ability of reinforcement learning, demonstrating sig...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Advances in Aeronautical Science and Engineering
2025-06-01
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| Series: | Hangkong gongcheng jinzhan |
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| Online Access: | http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2024049 |
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| _version_ | 1849469581516603392 |
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| author | LI Ni LIAN Yunxiao ZHOU Pan XIE Feng TANG Zhili ZHOU Haoran CHEN Jun |
| author_facet | LI Ni LIAN Yunxiao ZHOU Pan XIE Feng TANG Zhili ZHOU Haoran CHEN Jun |
| author_sort | LI Ni |
| collection | DOAJ |
| description | Major aviation nations and related research institutions are focusing on exploration and research of key technologies for intelligent air combat. Deep reinforcement learning combines the perceptual ability of deep learning with the decision-making ability of reinforcement learning, demonstrating significant advantages in the emergence of air combat capabilities. Based on the urgent needs of intelligent air combat development, the points of integration with the air combat field are explored by analyzing and summarizing the mainstream algorithms in the field of deep reinforcement learning. From the perspective of algorithm implementation, the key technologies of deep reinforcement learning in air combat are pointed out. By sorting out the current cutting-edge technological achievements in the field of air combat, it is concluded that the future research on deep reinforcement learning will develop from single-to-single air combat to cluster air combat. The challenges algorithm faced are proposed, which can provide the reference and guidance for the development of intelligent algorithms in intelligent air combat. |
| format | Article |
| id | doaj-art-8fc68d25ba714cb2bfd7c14c354e40ba |
| institution | Kabale University |
| issn | 1674-8190 |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Editorial Department of Advances in Aeronautical Science and Engineering |
| record_format | Article |
| series | Hangkong gongcheng jinzhan |
| spelling | doaj-art-8fc68d25ba714cb2bfd7c14c354e40ba2025-08-20T03:25:26ZzhoEditorial Department of Advances in Aeronautical Science and EngineeringHangkong gongcheng jinzhan1674-81902025-06-0116311610.16615/j.cnki.1674-8190.2025.03.01hkgcjz-16-3-1A survey of deep reinforcement learning technologies for intelligent air combatLI Ni0LIAN Yunxiao1ZHOU Pan2XIE Feng3TANG Zhili4ZHOU Haoran5CHEN Jun6School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, ChinaSchool of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, ChinaSchool of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, ChinaAVIC Chengdu Aircraft Design and Research Institute, Chengdu 610041, ChinaSchool of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, ChinaSchool of Cybersecurity, Northwestern Polytechnical University, Xi'an 710129, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, ChinaMajor aviation nations and related research institutions are focusing on exploration and research of key technologies for intelligent air combat. Deep reinforcement learning combines the perceptual ability of deep learning with the decision-making ability of reinforcement learning, demonstrating significant advantages in the emergence of air combat capabilities. Based on the urgent needs of intelligent air combat development, the points of integration with the air combat field are explored by analyzing and summarizing the mainstream algorithms in the field of deep reinforcement learning. From the perspective of algorithm implementation, the key technologies of deep reinforcement learning in air combat are pointed out. By sorting out the current cutting-edge technological achievements in the field of air combat, it is concluded that the future research on deep reinforcement learning will develop from single-to-single air combat to cluster air combat. The challenges algorithm faced are proposed, which can provide the reference and guidance for the development of intelligent algorithms in intelligent air combat.http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2024049intelligent air combatdeep reinforcement learningcombat aircraftkey technologiesdevelopment trend |
| spellingShingle | LI Ni LIAN Yunxiao ZHOU Pan XIE Feng TANG Zhili ZHOU Haoran CHEN Jun A survey of deep reinforcement learning technologies for intelligent air combat Hangkong gongcheng jinzhan intelligent air combat deep reinforcement learning combat aircraft key technologies development trend |
| title | A survey of deep reinforcement learning technologies for intelligent air combat |
| title_full | A survey of deep reinforcement learning technologies for intelligent air combat |
| title_fullStr | A survey of deep reinforcement learning technologies for intelligent air combat |
| title_full_unstemmed | A survey of deep reinforcement learning technologies for intelligent air combat |
| title_short | A survey of deep reinforcement learning technologies for intelligent air combat |
| title_sort | survey of deep reinforcement learning technologies for intelligent air combat |
| topic | intelligent air combat deep reinforcement learning combat aircraft key technologies development trend |
| url | http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2024049 |
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