文章基本信息

标题：Indirect Reinforcement Learning for Incident-responsive Ramp Control
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作者：Chao Lu ; Chao Lu ; Haibo Chen 等
期刊名称：Procedia - Social and Behavioral Sciences
印刷版ISSN：1877-0428
出版年度：2014
卷号：111
页码：1112-1122
DOI：10.1016/j.sbspro.2014.01.146
语种：English
出版社：Elsevier
摘要：AbstractA centralised strategy named indirect reinforcement learning ramp controller (IRLRC) has been developed in this paper to deal with ramp control problems for the congested traffic caused by incidents. IRLRC is developed on the basis of Dyna-Q architecture, under which a modified asymmetric cell transmission model (ACTM) and the standard Q-learning algorithm are combined together. The simulation-based test shows that compared with the no controlled situation, IRLRC can reduce the total travel time up to 24%, which outperforms the direct reinforcement learning (DRL) method with a reduction of 18% after the same number of iterations.
关键词：reinforcement learning;ramp metering;incident;cell transmission model