Reinforcement Learning Modeling of Missile Penetration Decision Based on Combat Simulation

Authors

Bin Zhang, College of College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China
Yonglin Lei, College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Qun Li, College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Yuan Gao, College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Yong Chen, College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Jiajun Zhu, College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Chenlong Bao, College of College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China

Abstract

Abstract: Penetration capability is a primary measure of missile systems. In response to the shortcomings of traditional knowledge-based decision-making methods that are difficult to adaptively evolve, an intelligent penetration decision-making based on combat simulation and DRL is proposed. A missile intelligent decision-making training environment is constructed based on the WESS system. Taking missile maneuver penetration decision-making as an example, a maneuver penetration decisionmaking network model is designed and trained based on the SAC-discrete algorithm and the test of intelligence is conducted. Experimental results show that the intelligent decision model derived from machine learning has a better combat outcome than traditional methods.

Recommended Citation

Zhang, Bin; Lei, Yonglin; Li, Qun; Gao, Yuan; Chen, Yong; Zhu, Jiajun; and Bao, Chenlong (2025) "Reinforcement Learning Modeling of Missile Penetration Decision Based on Combat Simulation," Journal of System Simulation: Vol. 37: Iss. 3, Article 18.
DOI: 10.16182/j.issn1004731x.joss.23-1397
Available at: https://dc-china-simulation.researchcommons.org/journal/vol37/iss3/18

First Page

763

Last Page

774

CLC

TP391.9

Recommended Citation

Zhang Bin, Lei Yonglin, Li Qun, et al. Reinforcement Learning Modeling of Missile Penetration Decision Based on Combat Simulation[J]. Journal of System Simulation, 2025, 37(3): 763-774.

Corresponding Author

Lei Yonglin

DOI

10.16182/j.issn1004731x.joss.23-1397