Reinforcement Learning Modeling of Missile Penetration Decision Based on Combat Simulation

被引：0

作者：

Zhang, Bin ^{[1
]}

Lei, Yonglin ^{[2
]}

Li, Qun ^{[2
]}

Gao, Yuan ^{[2
]}

Chen, Yong ^{[2
]}

Zhu, Jiajun ^{[2
]}

Bao, Chenlong ^{[1
]}

机构：

[1] College of College of Computer Science and Technology, National University of Defense Technology, Changsha

[2] College of Systems Engineering, National University of Defense Technology, Changsha

来源：

Xitong Fangzhen Xuebao / Journal of System Simulation | 2025年 / 37卷 / 03期

关键词：

combat simulation; DRL; intelligent decision-making; missile penetration; WESS simulation system;

D O I：

10.16182/j.issn1004731x.joss.23-1397

中图分类号：

学科分类号：

摘要：

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 decision-making 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. © 2025 Acta Simulata Systematica Sinica. All rights reserved.

引用

页码：763 / 774

页数：11

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