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Research on UCAV Maneuvering Decision Method Based on Heuristic Reinforcement Learning
With the rapid development of unmanned combat aerial vehicle (UCAV)-related technologies, UCAVs are playing an increasingly important role in military operations. It has become an inevitable trend in the development of future air combat battlefields that UCAVs complete air combat tasks independently...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8913150/ https://www.ncbi.nlm.nih.gov/pubmed/35281202 http://dx.doi.org/10.1155/2022/1477078 |
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author | Yuan, Wang Xiwen, Zhang Rong, Zhou Shangqin, Tang Huan, Zhou Wei, Ding |
author_facet | Yuan, Wang Xiwen, Zhang Rong, Zhou Shangqin, Tang Huan, Zhou Wei, Ding |
author_sort | Yuan, Wang |
collection | PubMed |
description | With the rapid development of unmanned combat aerial vehicle (UCAV)-related technologies, UCAVs are playing an increasingly important role in military operations. It has become an inevitable trend in the development of future air combat battlefields that UCAVs complete air combat tasks independently to acquire air superiority. In this paper, the UCAV maneuver decision problem in continuous action space is studied based on the deep reinforcement learning strategy optimization method. The UCAV platform model of continuous action space was established. Focusing on the problem of insufficient exploration ability of Ornstein–Uhlenbeck (OU) exploration strategy in the deep deterministic policy gradient (DDPG) algorithm, a heuristic DDPG algorithm was proposed by introducing heuristic exploration strategy, and then a UCAV air combat maneuver decision method based on a heuristic DDPG algorithm is proposed. The superior performance of the algorithm is verified by comparison with different algorithms in the test environment, and the effectiveness of the decision method is verified by simulation of air combat tasks with different difficulty and attack modes. |
format | Online Article Text |
id | pubmed-8913150 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-89131502022-03-11 Research on UCAV Maneuvering Decision Method Based on Heuristic Reinforcement Learning Yuan, Wang Xiwen, Zhang Rong, Zhou Shangqin, Tang Huan, Zhou Wei, Ding Comput Intell Neurosci Research Article With the rapid development of unmanned combat aerial vehicle (UCAV)-related technologies, UCAVs are playing an increasingly important role in military operations. It has become an inevitable trend in the development of future air combat battlefields that UCAVs complete air combat tasks independently to acquire air superiority. In this paper, the UCAV maneuver decision problem in continuous action space is studied based on the deep reinforcement learning strategy optimization method. The UCAV platform model of continuous action space was established. Focusing on the problem of insufficient exploration ability of Ornstein–Uhlenbeck (OU) exploration strategy in the deep deterministic policy gradient (DDPG) algorithm, a heuristic DDPG algorithm was proposed by introducing heuristic exploration strategy, and then a UCAV air combat maneuver decision method based on a heuristic DDPG algorithm is proposed. The superior performance of the algorithm is verified by comparison with different algorithms in the test environment, and the effectiveness of the decision method is verified by simulation of air combat tasks with different difficulty and attack modes. Hindawi 2022-03-03 /pmc/articles/PMC8913150/ /pubmed/35281202 http://dx.doi.org/10.1155/2022/1477078 Text en Copyright © 2022 Wang Yuan et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Yuan, Wang Xiwen, Zhang Rong, Zhou Shangqin, Tang Huan, Zhou Wei, Ding Research on UCAV Maneuvering Decision Method Based on Heuristic Reinforcement Learning |
title | Research on UCAV Maneuvering Decision Method Based on Heuristic Reinforcement Learning |
title_full | Research on UCAV Maneuvering Decision Method Based on Heuristic Reinforcement Learning |
title_fullStr | Research on UCAV Maneuvering Decision Method Based on Heuristic Reinforcement Learning |
title_full_unstemmed | Research on UCAV Maneuvering Decision Method Based on Heuristic Reinforcement Learning |
title_short | Research on UCAV Maneuvering Decision Method Based on Heuristic Reinforcement Learning |
title_sort | research on ucav maneuvering decision method based on heuristic reinforcement learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8913150/ https://www.ncbi.nlm.nih.gov/pubmed/35281202 http://dx.doi.org/10.1155/2022/1477078 |
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