Imitative Generation of Optimal Guidance Law Based on Reinforcement Learning

Authors

Zhengxuan Jia, Beijing Simulation Center, Beijing 100854, ChinaFollow
Tingyu Lin, Beijing Simulation Center, Beijing 100854, China
Yingying Xiao, Beijing Simulation Center, Beijing 100854, China
Guoqiang Shi, Beijing Simulation Center, Beijing 100854, China
Hao Wang, Beijing Institute of Electronic System Engineering, Beijing 100854, China
Bi Zeng, Beijing Institute of Electronic System Engineering, Beijing 100854, China
Yiming Ou, Beijing Simulation Center, Beijing 100854, China
Pengpeng Zhao, Beijing Simulation Center, Beijing 100854, China

Abstract

Abstract: Under the background of high-speed maneuvering target interception, an optimal guidance law generation method for head-on interception independent of target acceleration estimation is proposed based on deep reinforcement learning. In addition, its effectiveness is verified through simulation experiments. As the simulation results suggest, the proposed method successfully achieves head-on interception of high-speed maneuvering targets in 3D space and largely reduces the requirement for target estimation with strong uncertainty, and it is more applicable than the optimal control method.

Recommended Citation

Jia, Zhengxuan; Lin, Tingyu; Xiao, Yingying; Shi, Guoqiang; Wang, Hao; Zeng, Bi; Ou, Yiming; and Zhao, Pengpeng (2023) "Imitative Generation of Optimal Guidance Law Based on Reinforcement Learning," Journal of System Simulation: Vol. 35: Iss. 11, Article 10.
DOI: 10.16182/j.issn1004731x.joss.22-0632
Available at: https://dc-china-simulation.researchcommons.org/journal/vol35/iss11/10

First Page

2410

Last Page

2418

CLC

TP391.9

Recommended Citation

Jia Zhengxuan, Lin Tingyu, Xiao Yingying, et al. Imitative Generation of Optimal Guidance Law Based on Reinforcement Learning[J]. Journal of System Simulation, 2023, 35(11): 2410-2418.

DOI

10.16182/j.issn1004731x.joss.22-0632