Research on the Application of Reinforcement Learning Algorithm in Decision Support of Beyond-Visual-Range Air Combat

In order to solve problems of the action selection space and the difficulty of convergence of traditional proximal policy optimization algorithm in air combat simulation, proximal policy hierarchical optimization algorithm is proposed. The framework of intelligent decision model of air combat based...

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Autor principal:	Wu Yijia, Lai Jun, Chen Xiliang, Cao Lei, Xu Peng
Formato:	article
Lenguaje:	ZH
Publicado:	Editorial Office of Aero Weaponry 2021
Materias:	\|beyond-visual-range air combat\|intelligent decision\|artificial intelligence\|reinforcement learning\|proximal policy optimization algorithm\|hierarchical reinforcement learning Motor vehicles. Aeronautics. Astronautics TL1-4050
Acceso en línea:	https://doaj.org/article/9f7cc1baef144ea29add1b321d811e83
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id	oai:doaj.org-article:9f7cc1baef144ea29add1b321d811e83
record_format	dspace
spelling	oai:doaj.org-article:9f7cc1baef144ea29add1b321d811e832021-11-30T00:13:49ZResearch on the Application of Reinforcement Learning Algorithm in Decision Support of Beyond-Visual-Range Air Combat1673-504810.12132/ISSN.1673-5048.2020.0076https://doaj.org/article/9f7cc1baef144ea29add1b321d811e832021-04-01T00:00:00Zhttps://www.aeroweaponry.avic.com/fileup/1673-5048/PDF/2020-00076.pdfhttps://doaj.org/toc/1673-5048In order to solve problems of the action selection space and the difficulty of convergence of traditional proximal policy optimization algorithm in air combat simulation, proximal policy hierarchical optimization algorithm is proposed. The framework of intelligent decision model of air combat based on reinforcement learning is constructed, and the antagonistic experiment is carried out and visualized. The experimental result shows that proximal policy hierarchical optimization algorithm could drive the agent to produce indirect attack and other tactical behaviors in the process of confrontation. The purpose of improving the performance of the traditional algorithm and decision-making efficiency of air combat is achieved.Wu Yijia, Lai Jun, Chen Xiliang, Cao Lei, Xu PengEditorial Office of Aero Weaponryarticle\|beyond-visual-range air combat\|intelligent decision\|artificial intelligence\|reinforcement learning\|proximal policy optimization algorithm\|hierarchical reinforcement learningMotor vehicles. Aeronautics. AstronauticsTL1-4050ZHHangkong bingqi, Vol 28, Iss 2, Pp 55-61 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	\|beyond-visual-range air combat\|intelligent decision\|artificial intelligence\|reinforcement learning\|proximal policy optimization algorithm\|hierarchical reinforcement learning Motor vehicles. Aeronautics. Astronautics TL1-4050
spellingShingle	\|beyond-visual-range air combat\|intelligent decision\|artificial intelligence\|reinforcement learning\|proximal policy optimization algorithm\|hierarchical reinforcement learning Motor vehicles. Aeronautics. Astronautics TL1-4050 Wu Yijia, Lai Jun, Chen Xiliang, Cao Lei, Xu Peng Research on the Application of Reinforcement Learning Algorithm in Decision Support of Beyond-Visual-Range Air Combat
description	In order to solve problems of the action selection space and the difficulty of convergence of traditional proximal policy optimization algorithm in air combat simulation, proximal policy hierarchical optimization algorithm is proposed. The framework of intelligent decision model of air combat based on reinforcement learning is constructed, and the antagonistic experiment is carried out and visualized. The experimental result shows that proximal policy hierarchical optimization algorithm could drive the agent to produce indirect attack and other tactical behaviors in the process of confrontation. The purpose of improving the performance of the traditional algorithm and decision-making efficiency of air combat is achieved.
format	article
author	Wu Yijia, Lai Jun, Chen Xiliang, Cao Lei, Xu Peng
author_facet	Wu Yijia, Lai Jun, Chen Xiliang, Cao Lei, Xu Peng
author_sort	Wu Yijia, Lai Jun, Chen Xiliang, Cao Lei, Xu Peng
title	Research on the Application of Reinforcement Learning Algorithm in Decision Support of Beyond-Visual-Range Air Combat
title_short	Research on the Application of Reinforcement Learning Algorithm in Decision Support of Beyond-Visual-Range Air Combat
title_full	Research on the Application of Reinforcement Learning Algorithm in Decision Support of Beyond-Visual-Range Air Combat
title_fullStr	Research on the Application of Reinforcement Learning Algorithm in Decision Support of Beyond-Visual-Range Air Combat
title_full_unstemmed	Research on the Application of Reinforcement Learning Algorithm in Decision Support of Beyond-Visual-Range Air Combat
title_sort	research on the application of reinforcement learning algorithm in decision support of beyond-visual-range air combat
publisher	Editorial Office of Aero Weaponry
publishDate	2021
url	https://doaj.org/article/9f7cc1baef144ea29add1b321d811e83
work_keys_str_mv	AT wuyijialaijunchenxiliangcaoleixupeng researchontheapplicationofreinforcementlearningalgorithmindecisionsupportofbeyondvisualrangeaircombat
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Research on the Application of Reinforcement Learning Algorithm in Decision Support of Beyond-Visual-Range Air Combat

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