计算机与数字工程2019，Vol.47Issue(4)：812-819,8.DOI:10.3969/j.issn.1672-9722.2019.04.014
一种基于稠密卷积网络和竞争架构的改进路径规划算法
An Improved Path Planning Algorithm Based on Densely Connected Convolutional Network and Dueling Network Architecture
摘要
Abstract
When the existing deep Q-networks are used in path planning domain,they surfer with overestimations of ac?tion-state values and can't meet the need for real-time path planning. So,an improved path planning algorithm based on densely connected convolutional network and dueling network architecture is proposed. Firstly,a network that fused simplified densely con?nected convolutional network with dueling network architecture is proposed,which is a lighter deep network for deep reinforcement learning. Then reinforcement learning methods are used to solve the path planning problem and train the proposed network by double deep Q-network(DDQN)algorithm to approximate the optimal action-state value function. Finally,experiments in the customized gridmap environment are done. Experiments demonstrate that our proposed algorithm can not only obtain less parameters,computa?tion time and lower training expense,and meet the need for real-time path planning,but also can lead to more state-of-the-art per?formance on route planning domain which can increase the path planning success rate by about 5% on average with great generaliza?tion ability of rapidly changing environments.关键词
深度强化学习/路径规划/稠密卷积网络/竞争网络架构/双重深度Q网络Key words
deep reinforcement learning/path planning/densely connected convolutional network/dueling network archi⁃tecture/double deep Q-network分类
信息技术与安全科学引用本文复制引用
黄颖,余玉琴..一种基于稠密卷积网络和竞争架构的改进路径规划算法[J].计算机与数字工程,2019,47(4):812-819,8.基金项目
国家自然科学基金(编号:61603255,61673276)资助. （编号:61603255,61673276）
