Research on Autonomous Maneuvering Decision of UCAV Based on Deep Reinforcement Learning | |
Zhang, Yesheng1,2; Zu, Wei1; Gao, Yang1; Chang, Hongxing1 | |
2018-06 | |
会议名称 | The 30th Chinese Control and Decision Conference |
会议录名称 | CCDC2018 |
卷号 | 1 |
页码 | 230-235 |
会议日期 | June 9-11, 2018 |
会议地点 | Shenyang, China |
出版地 | Singapore |
会议主办者 | 东北大学 |
出版者 | IEEE Industrial Electronics (IE) Chapter, Singapore |
摘要 |
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. |
关键词 | Air Combat Autonomous Maneuvering Decision Deep Reinforcement Learning |
学科领域 | Autonomous Control |
URL | 查看原文 |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/20920 |
专题 | 综合信息系统研究中心 |
通讯作者 | Zhang, Yesheng |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 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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