控制理论与应用2024，Vol.41Issue(6)：990-998,9.DOI:10.7641/CTA.2023.20696
基于深度强化学习的舰船导弹目标分配方法
Missile-target assignment method of naval ship based on deep reinforcement learning
肖友刚 1金升成 1毛晓 1伍国华 1陆志沣2
作者信息
- 1. 中南大学交通运输工程学院,湖南长沙 410018
- 2. 上海机电工程研究所,上海 201109
- 折叠
摘要
Abstract
To effectively solve the missile-target allocation problem of the naval ship in the case of confrontation,this study proposes a deep reinforcement learning algorithm combining attention mechanism.First,we construct a mathematical model for multi-type missiles of the naval ship and design the Markov decision-making process considering the situation of multi-times target interception.After that,the policy network is constructed based on the encoder-decoder architecture,in which targets are encoded combined with the multi-head attention mechanism and the reasonable missile-target allocation scheme is generated in the decoder according to integrated global and local embedding information.Finally,we conduct simulation experiments are carried out on the profit of missile-target allocation schemes,computation time,and the training process of the policy network.The experimental results show that our algorithm can engender missile-target allocation schemes with higher profit compared to baselines,the computation time in large-scale problems is reduced by 10％～94％,and it converges fast and stably.关键词
防空反导/导弹目标分配/武器目标分配/深度强化学习Key words
air defense and anti-missile/missile-target allocation/weapon-target allocation/deep reinforcement learn-ing引用本文复制引用
肖友刚,金升成,毛晓,伍国华,陆志沣..基于深度强化学习的舰船导弹目标分配方法[J].控制理论与应用,2024,41(6):990-998,9.
