文章基本信息

标题：A Deep Reinforcement Learning Approach for Autonomous Highway Driving
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作者：Junwu Zhao ; Ting Qu ; Fang Xu 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：5
页码：542-546
DOI：10.1016/j.ifacol.2021.04.142
语种：English
出版社：Elsevier
摘要：AbstractAutonomous driving has been the trend. In this paper, a Deep Reinforcement Learning (DRL) method is exploited to model the decision making and interaction between vehicles on highway driving. To avoid the overestimate action values induced by Q-learning, we use the Double Deep Q-Network (DDQN) for the training of the host vehicle. The agent learns from trial and interactions with the environment. A simulation platform based on the Simulation of Urban Mobility (SUMO) is also established, it helps facilitate the variation of control algorithms. The results show that the proposed framework can simulate highway driving, and the trained agent can accomplish the driving task with ease after training and can approximate the highest safe driving speed as defined without collision.
关键词：KeywordsAutonomous DrivingDeep Reinforcement LearningVehicle ControlTraffic ModelingTraffic Simulator