指挥控制与仿真2024，Vol.46Issue(4)：35-43,9.DOI:10.3969/j.issn.1673-3819.2024.04.005
基于强化学习的智能空战模型研究
Research of intelligent air combat model based on reinforcement learning
摘要
Abstract
The development in artificial intelligence has dramatically changed all industries,among which AI-assisted air combat is a representative case of success.An Intelligent air combat model that consists of the attainment of samples and a decision-making model is constructed in connection with air combat simulator.Considering the characteristics of air combat continuous states and actions,DQN algorithm is selected as the model of intelligent air combat by comparison among several algorithms.Meanwhile,the AI network is trained interactively with AI enemies in the air combat simulation game DCS World,resulting in a model that is able to manipulate aircraft to a degree and many cases of air combat,by analyzing which a collection of winning,losing and dual samples is derived.The result of simulation indicates that the Intelligent air combat model has certain ability to generate strategic samples and enrich tactics in air combat environments.关键词
空战/强化学习/飞行模拟游戏Key words
air combat/deep reinforcement learning/flight simulation game分类
信息技术与安全科学引用本文复制引用
李佳桐,卢俊元,王光耀,李建勋..基于强化学习的智能空战模型研究[J].指挥控制与仿真,2024,46(4):35-43,9.基金项目
重点研发计划(2020YFC1512203) （2020YFC1512203）
上海商用飞机系统工程联合研究基金(CASEF-2022-MQ01) （CASEF-2022-MQ01）
