文章基本信息

标题：Motion Planning at Intersections with Event-driven Recurrent Q-Learning
本地全文：下载
作者：Xinze Jin ; Qing-Shan Jia ; Dongchun Ren 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：17047-17052
DOI：10.1016/j.ifacol.2020.12.1533
语种：English
出版社：Elsevier
摘要：AbstractAutonomous driving at intersection has great potential on control for smart cities to relieve the energy consumption and transportation congestion. However, it remains challenging to find promising behavior sequence in multi-agent environment with uncertain participation of obstacles. This work develops Event-driven Recurrent Q-Learning (ERQL) to focus on the motion planning task towards intersection scenarios to conclude a sample path with safety and efficiency. We elaborate the definition of events to capture the environment structure and introduce recurrency to process sequence model. Besides, we incorporate collision-avoidance into the event-driven framework and design a mechanism to extract recurrent feature from replay buffer in Q-learning framework. Simulation results show that the developed off-line learning procedure can adapt to on-line decision making towards uncertain agent behaviors.
关键词：Keywordsmotion planningreinforcement learningevent-drivenrecurrency