Traffic Signal Control Based on Deep Reinforcement Learning with Simplified State and Reward Definitions

Salah Bouktif, Abderraouf Cheniki, Ali Ouni, Hesham El-Sayed

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    Traffic congestion has recently become a real issue especially within crowded cities and urban areas. Intelligent transportation systems (ITS) leveraged various advanced techniques aiming to optimize the traffic flow and subsequently alleviate the traffic congestion. In particular, traffic signal control TSC is one of the essential ITS techniques for controlling the traffic flow at intersections. Many research works have been proposed to develop algorithms and techniques which optimize TSC behavior. Recent works leverage Deep Learning (DL) and Reinforcement Learning (RL) techniques to optimize TSCs. However, most of Deep RL proposals are based on complex definitions of state and reward in the RL framework. In this work, we propose to use an alternative way of formulating the state and reward definitions. Basically, The basic idea is to define both state and reward in a simplified and straightforward manner rather than the complex design. We hypothesize that such a design approach simplifies the learning of the RL agent and hence provides a rapid convergence to optimal policies. For the agent architecture, we employ the double deep Q-Network (DDQN) along with prioritized experience replay (PER). We conduct the experiments using the Simulation of Urban MObility (SUMO) simulator interfaced with Python framework and we compare the performance of our proposal to traditional and learning-based techniques.

    Original languageEnglish
    Title of host publication2021 4th International Conference on Artificial Intelligence and Big Data, ICAIBD 2021
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages253-260
    Number of pages8
    ISBN (Electronic)9780738131702
    DOIs
    Publication statusPublished - May 28 2021
    Event4th International Conference on Artificial Intelligence and Big Data, ICAIBD 2021 - Chengdu, China
    Duration: May 28 2021May 31 2021

    Publication series

    Name2021 4th International Conference on Artificial Intelligence and Big Data, ICAIBD 2021

    Conference

    Conference4th International Conference on Artificial Intelligence and Big Data, ICAIBD 2021
    Country/TerritoryChina
    CityChengdu
    Period5/28/215/31/21

    Keywords

    • Double DQN
    • Reinforcement Learning
    • Traffic Optimization
    • Traffic Signal Control

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Networks and Communications
    • Information Systems
    • Information Systems and Management
    • Decision Sciences (miscellaneous)
    • Safety, Risk, Reliability and Quality

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