Distributed DDPG UAV pursuit decision based on angle feature
WANG Yu
REN Tian-jun
FAN Zi-lin
MENG Guang-lei
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.
Keywords：pursuit decision-makingreinforcement learningdistributed DDPG algorithmangle feature
Publication Date：2025-07-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 1356-1366 )