控制理论与应用2025，Vol.42Issue(7)：1356-1366,11.DOI:10.7641/CTA.2024.30105
基于角度特征的分布式DDPG无人机追击决策
Distributed DDPG UAV pursuit decision based on angle feature
摘要
Abstract
The situation of the UAV changes rapidly during the pursuit mission.The inflexible network update mech-anism and the fixed reward function make it difficult for the existing decision model to continuously output correct and efficient strategies.To solve this problem,a distributed deep deterministic policy gradient(DDPG)algorithm based on angle feature is proposed.Firstly,to avoid gradient disappearing or exploding,stabilize the training process of the model,a parameter update mechanism of Actor network is proposed,which uses gradient ascent to calculate the target value of Actor network,and then trains Actor network with the mean-square error(MSE)loss function.Then,the strategy guidance area is divided according to the situation of both sides.By assigning different weights to the reward function,a distributed decision-making model is built based on five DDPG networks.Using the dynamic selection and seamless switching of reward function weights under different situations,the decision-making ability of the algorithm is improved.Simulation results show that comparing with the algorithms of DDPG and twin delayed deep deterministic policy gradient(TD3),the proposed algorithm has a higher success rate and higher decision-making efficiency when pursuing the linear escape target or the intelligent escape target.关键词
追击决策/强化学习/分布式DDPG算法/角度特征Key words
pursuit decision-making/reinforcement learning/distributed DDPG algorithm/angle feature引用本文复制引用
王昱,任田君,范子琳,孟光磊..基于角度特征的分布式DDPG无人机追击决策[J].控制理论与应用,2025,42(7):1356-1366,11.基金项目
国家自然科学基金项目(61906125,62373261),辽宁省属本科高校基本科研业务费专项基金项目(LJ232410143020,LJ212410143047)资助.Supported by the National Natural Science Foundation of China(61906125,62373261)and the Fundamental Research Funds for the Universities of Liaoning Province(LJ232410143020,LJ212410143047). （61906125,62373261）
