火力与指挥控制2024，Vol.49Issue(8)：166-174,9.DOI:10.3969/j.issn.1002-0640.2024.08.022
基于D3QN的火力方案优选方法
Optimization Selection Method of Fire Plan Based on D3QN
摘要
Abstract
To address the problem of inefficient fire plan optimization in the task of coordinated attack on ground fortification-type targets by multiple types of munitions,a fire plan optimization method based on the Dueling Double Deep Q Network(D3QN)is proposed.The method models the striking process as Markov Decision Processes(MDPs),firstly its state space and action space are designed,then a comprehensive reward function is designed to stimulate the optimization of the fire plan generation strategy,and finally the intelligent body is enabled to train the strategy autonomously through a reinforcement learning framework.The simulation experiment results show that the method can achieve more optimal fire solutions for ground fortification type targets than that of the traditional heuristic intelligence algorithms,and its computational efficiency and stability of results are more obvi-ously advantageous than that of the traditional deep reinforcement learning algorithms.关键词
深度强化学习/深度Q网络/D3QN/组合优化/火力方案优选Key words
deep reinforcement learning/deep Q network/D3QN/combinatorial optimization prob-lem/optimization of fire plan分类
军事科技引用本文复制引用
佘维,岳瀚,田钊,孔德锋..基于D3QN的火力方案优选方法[J].火力与指挥控制,2024,49(8):166-174,9.基金项目
嵩山实验室预研项目(YYYY022022003) （YYYY022022003）
河南省重点研发与推广专项基金资助项目(212102310039) （212102310039）
