基于深度强化学习的公路初始路径寻优方法  

An optimal path generation method based on deep reinforcement learning

作　　者：李懿 韩春华[1] 钱熙 孟靖凯 LI Yi;HAN Chun-hua;QIAN Xi;MEN Jing-kai(Faculty of Transportation Engineering,Kunming University of Science and Technology,Kunming 650500,China)

机构地区：[1]昆明理工大学交通工程学院,云南昆明650500

出　　处：《交通科学与工程》2020年第1期98-103,共6页Journal of Transport Science and Engineering

基　　金：国家自然科学基金资助项目(51268023);云南省应用基础研究计划面上项目(2015FB124)。

摘　　要：针对人工选线存在着工作量大、周期长,且人工估算路线平、纵、横的方式与实际最优路径存在着偏差的缺陷,提出了一种智能的、通过不断试错学习人工选择线路经验完成任务的方法。该方法将Q-learning算法运用于神经网络中,采用深度Q学习路线经验,使神经网络能够快速地达到收敛,最终生成一条具有环境适应能力的最优路径。通过实例证明,该路线具有实用性和有效性。In view of the huge workload and long cycle of manual line selection at present,as well as the deviation among the horizontal,vertical and horizontal way of line estimation and the actual optimal path.A method for the agent to complete line selection through trial and error and learning manual route selection experience is proposed.In this method,Q-learning algorithm is applied to neural network,and deep Q-learning is used to learn circuit experience,so that the neural network can quickly reach convergence,and finally generate an optimal path with environmental adaptability.Compared with the example,it is proved that the route is practical and effective.

关 键 词：智能选线 最优路径 强化学习 公路选线 

分 类 号：U491[交通运输工程—交通运输规划与管理]