A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 3 Issue 3
Jul.  2016

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Li Li, Yisheng Lv and Fei-Yue Wang, "Traffic Signal Timing via Deep Reinforcement Learning," IEEE/CAA J. of Autom. Sinica, vol. 3, no. 3, pp. 247-254, 2016.
Citation: Li Li, Yisheng Lv and Fei-Yue Wang, "Traffic Signal Timing via Deep Reinforcement Learning," IEEE/CAA J. of Autom. Sinica, vol. 3, no. 3, pp. 247-254, 2016.

Traffic Signal Timing via Deep Reinforcement Learning

Funds:

This work was supported by National Natural Science Foundation of China (61533019, 71232006, 61233001).

More Information
  • 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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