Deep reinforcement learning for traffic signal control with consistent state and reward design approach

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

    Research output: Contribution to journalArticlepeer-review

    15 Citations (Scopus)

    Abstract

    Intelligent Transportation Systems are essential due to the increased number of traffic congestion problems and challenges nowadays. Traffic Signal Control (TSC) plays a critical role in optimizing the traffic flow and mitigating the congestion within the urban areas. Various research works have been conducted to enhance the behavior of TSCs at intersections and subsequently reduce the traffic congestion. Researchers recently leveraged Deep Learning (DL) and Reinforcement Learning (RL) techniques to optimize TSCs. In RL framework, the agent interacts with surrounding world through states, rewards and actions. The formulation of these key elements is crucial as they impact the way the RL agent behaves and optimizes its policy. However, most of existing frameworks rely on hand-crafted state and reward designs, restricting the RL agent from acting optimally. In this paper, we propose a novel approach to better formulate state and reward definitions in order to boost the performance of the traffic signal controller agent. The intuitive idea is to define both state and reward in a consistent and straightforward manner. We advocate that such a design approach helps achieving training stability and hence provides a rapid convergence to derive best policies. We consider the double deep Q-Network (DDQN) along with prioritized experience replay (PER) for the agent architecture. To evaluate the performance of our approach, we conduct series of simulations using the Simulation of Urban MObility (SUMO) environment. The statistical analysis of our results show that the performance of our proposal outperforms the state-of-the-art state and reward design approaches.

    Original languageEnglish
    Article number110440
    JournalKnowledge-Based Systems
    Volume267
    DOIs
    Publication statusPublished - May 12 2023

    Keywords

    • Double deep Q-Network
    • Reinforcement learning
    • Traffic optimization
    • Traffic signal control

    ASJC Scopus subject areas

    • Software
    • Management Information Systems
    • Information Systems and Management
    • Artificial Intelligence

    Fingerprint

    Dive into the research topics of 'Deep reinforcement learning for traffic signal control with consistent state and reward design approach'. Together they form a unique fingerprint.

    Cite this