摘要
突防能力是导弹等装备的关键评价指标,针对传统基于知识工程的突防决策方法难以自适应演进的不足,提出了基于作战仿真和深度强化学习结合的智能突防决策建模方法。搭建了基于WESS的导弹智能决策训练环境;以导弹机动突防决策建模为例进行了应用研究,建立了机动突防决策网络模型;基于离散SAC算法进行了决策模型的强化学习训练,并开展智能化测试对比。初步试验结果表明:基于机器学习的智能决策模型具有更好的突防效果。
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.
作者
张斌
雷永林
李群
高远
陈永
朱佳俊
鲍琛龙
Zhang Bin;Lei Yonglin;Li Qun;Gao Yuan;Chen Yong;Zhu Jiajun;Bao Chenlong(College of College of Computer Science and Technology,National University of Defense Technology,Changsha 410073,China;College of Systems Engineering,National University of Defense Technology,Changsha 410073,China)
出处
《系统仿真学报》
北大核心
2025年第3期763-774,共12页
Journal of System Simulation
作者简介
第一作者:张斌(1976-),男,研究员,博士,研究方向为装备知识工程;通信作者:雷永林(1978-),男,教授,博导,博士,研究方向为作战效能仿真。
