期刊
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
卷 3, 期 3, 页码 247-254出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/jas.2016.7508798
关键词
Traffic control; reinforcement learning; deep learning; deep reinforcement learning
资金
- National Natural Science Foundation of China [61533019, 71232006, 61233001]
In this paper, we propose a set of algorithms to design signal timing plans via deep reinforcement learning. The core idea of this approach is to set up a deep neural network (DNN) to learn the Q-function of reinforcement learning from the sampled traffic state/control inputs and the corresponding traffic system performance output. Based on the obtained DNN, we can find the appropriate signal timing policies by implicitly modeling the control actions and the change of system states. We explain the possible benefits and implementation tricks of this new approach. The relationships between this new approach and some existing approaches are also carefully discussed.
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