Missile guidance with assisted deep reinforcement learning for head-on interception of maneuvering target
文献类型：期刊论文
| 作者 | Li, Weifan1,2 ; Zhu, Yuanheng1,2; Zhao, Dongbin1,2
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| 刊名 | COMPLEX & INTELLIGENT SYSTEMS
 |
| 出版日期 | 2021-11-24 |
| 页码 | 12 |
| 关键词 | Reinforcement learning Missile guidance Auxiliary learning Self-imitation learning |
| ISSN号 | 2199-4536 |
| DOI | 10.1007/s40747-021-00577-6 |
| 通讯作者 | Zhu, Yuanheng(yuanheng.zhu@ia.ac.cn) |
| 英文摘要 | In missile guidance, pursuit performance is seriously degraded due to the uncertainty and randomness in target maneuverability, detection delay, and environmental noise. In many methods, accurately estimating the acceleration of the target or the time-to-go is needed to intercept the maneuvering target, which is hard in an environment with uncertainty. In this paper, we propose an assisted deep reinforcement learning (ARL) algorithm to optimize the neural network-based missile guidance controller for head-on interception. Based on the relative velocity, distance, and angle, ARL can control the missile to intercept the maneuvering target and achieve large terminal intercept angle. To reduce the influence of environmental uncertainty, ARL predicts the target's acceleration as an auxiliary supervised task. The supervised learning task improves the ability of the agent to extract information from observations. To exploit the agent's good trajectories, ARL presents the Gaussian self-imitation learning to make the mean of action distribution approach the agent's good actions. Compared with vanilla self-imitation learning, Gaussian self-imitation learning improves the exploration in continuous control. Simulation results validate that ARL outperforms traditional methods and proximal policy optimization algorithm with higher hit rate and larger terminal intercept angle in the simulation environment with noise, delay, and maneuverable target. |
| WOS关键词 | IMPACT TIME ; LAW ; DESIGN |
| 资助项目 | National Key Research and Development Program of China[2018AAA0101005] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA27030400] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences[2021132] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000722088600001 |
| 出版者 | SPRINGER HEIDELBERG |
| 资助机构 | National Key Research and Development Program of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; Youth Innovation Promotion Association of the Chinese Academy of Sciences |
| 源URL | [http://ir.ia.ac.cn/handle/173211/46551]  |
| 专题 | 复杂系统管理与控制国家重点实验室_深度强化学习 |
| 通讯作者 | Zhu, Yuanheng |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Weifan,Zhu, Yuanheng,Zhao, Dongbin. Missile guidance with assisted deep reinforcement learning for head-on interception of maneuvering target[J]. COMPLEX & INTELLIGENT SYSTEMS,2021:12. |
| APA | Li, Weifan,Zhu, Yuanheng,&Zhao, Dongbin.(2021).Missile guidance with assisted deep reinforcement learning for head-on interception of maneuvering target.COMPLEX & INTELLIGENT SYSTEMS,12. |
| MLA | Li, Weifan,et al."Missile guidance with assisted deep reinforcement learning for head-on interception of maneuvering target".COMPLEX & INTELLIGENT SYSTEMS (2021):12. |
入库方式： OAI收割
来源：自动化研究所
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