计算机应用与软件2025，Vol.42Issue(6)：335-341,7.DOI:10.3969/j.issn.1000-386x.2025.06.044
基于改进DQN算法的移动机器人路径规划
MOBILE ROBOT PATH PLANNING BASED ON IMPROVED DQN ALGORITHM
摘要
Abstract
It is important to guarantee real-time performance when the path planning is carried out in a dynamic and complex environment.Aimed at the overestimation problem and the slow convergence speed of DQN algorithm in mobile robot path planning,a C-RD3QN(combination-residual dueling double DQN)algorithm is proposed.Based on D3QN algorithm,the convolution layer was modified to the residual network structure,the action advantage function in the competitive network structure was used to estimate the action value function,and the state value function was combined with the reward value to achieve faster convergence speed.The simulation results show that the C-RD3QN algorithm can carry out better path planning.关键词
深度强化学习/机器人路径规划/残差网络结构/奖励值重构Key words
Deep reinforcement learning/Robot path planning/Residual network structure/Reward value recon-struction分类
信息技术与安全科学引用本文复制引用
于效民,王欣,吴迪,刘雪莲..基于改进DQN算法的移动机器人路径规划[J].计算机应用与软件,2025,42(6):335-341,7.基金项目
国家工信部、卫健委的5G+医疗健康应用试点项目(面向5G的智能搬运机器人应用试点). （面向5G的智能搬运机器人应用试点）
