%0 Journal Article %A SHU Lingzhou %A WANG Chen %A WU Jia %T Urban traffic signal control based on deep reinforcement learning %D 2019 %R 10.11772/j.issn.1001-9081.2018092015 %J Journal of Computer Applications %P 1495-1499 %V 39 %N 5 %X To meet the requirements for adaptivity, and robustness of the algorithm to optimize urban traffic signal control, a traffic signal control algorithm based on Deep Reinforcement Learning (DRL) was proposed to control the whole regional traffic with a control Agent contructed by a deep learning network. Firstly, the Agent predicted the best possible traffic control strategy for the current state by observing continously the state of the traffic environment with an abstract representation of a location matrix and a speed matrix, because the matrix representation method can effectively abstract vital information and reduce redundant information about the traffic environment. Then, based on the impact of the strategy selected on the traffic environment, a reinforcement learning algorithm was employed to correct the intrinsic parameters of the Agent constantly in order to maximize the global speed in a period of time. Finally, after several iterations, the Agent learned how to effectively control the traffic.The experiments in the traffic simulation software Vissim show that compared with other algorithms based on DRL, the proposed algorithm is superior in average global speed, average queue length and stability; the average global speed increases 9% and the average queue length decreases 13.4% compared to the baseline. The experimental results verify that the proposed algorithm can adapt to complex and dynamically changing traffic environment. %U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2018092015