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Research on Autonomous Maneuvering Decision of UCAV Based on Deep Reinforcement Learning
Zhang, Yesheng1,2; Zu, Wei1; Gao, Yang1; Chang, Hongxing1
2018-06
Conference NameThe 30th Chinese Control and Decision Conference
Source PublicationCCDC2018
Volume1
Pages230-235
Conference DateJune 9-11, 2018
Conference PlaceShenyang, China
Publication PlaceSingapore
Funding Organization东北大学
PublisherIEEE Industrial Electronics (IE) Chapter, Singapore
Abstract
In order to improve the intelligent level of UCAV in one-to-one air combat, an autonomous maneuvering decision algorithm based on deep reinforcement learning is proposed. UCAV learns strategies by sensing the environment, performing maneuvering actions, and getting feedback. In this way, we can avoid the limitations of existing theories and human operations. Firstly an environment is modeled to simulate the real-time situation of air combat. Then a situation assessment method based on Energy-Maneuverability theory is utilized to design the reward functions. Finally model based on deep reinforcement learning is created for UCAV to learn strategies to gain the advantage for the opponent.
KeywordAir Combat Autonomous Maneuvering Decision Deep Reinforcement Learning
Subject AreaAutonomous Control
URL查看原文
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20920
Collection综合信息系统研究中心
Corresponding AuthorZhang, Yesheng
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhang, Yesheng,Zu, Wei,Gao, Yang,et al. Research on Autonomous Maneuvering Decision of UCAV Based on Deep Reinforcement Learning[C]. Singapore:IEEE Industrial Electronics (IE) Chapter, Singapore,2018:230-235.
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