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Title	Introduction To Coordinated Deep Agents For Traffic Signal
ID_Doc	33264
Authors	Reda M.; Mountassir F.; Mohamed B.
Year	2019
Published	2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems, WITS 2019
DOI	http://dx.doi.org/10.1109/WITS.2019.8723846
Abstract	Traffic Signal control optimization is a key element is future smart cities. We present, in this paper, an optimized agent, based on Reinforcement Learning, with Double Deep Q-Learning. Since intersections influence each other, cooperation between agents will be key in our study. For state representation, we use real-time data, that can be collected by a smart camera, as vehicle position and speed. The rewards function will consider the waiting time of drivers, which has the advantage of better representing the case of congestions in large agglomerations. In our network, we will use Convolutional Layers to extract features that will help our model to learn the best actions to take, in every state. This model will be used to solve convergence and oscillation problems that commonly appears in multi-agent context with Neuronal Networks. A future article will present the details of the implementation and the results obtained and will make a comparison between our model and other state-of-art solutions used in the Traffic Signal Control. © 2019 IEEE.
Author Keywords	Deep Reinforcement Learning; Trafc Signal Control

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