Reinforcement Learning (RL) is being rapidly adopted in many complex environments due to its ability to leverage neural networks to learn good strategies. In traffic signal control (TSC), existing work has focused on off-policy learning (Q-learning) with neural networks. There is limited study on on-policy learning (SARSA) with neural networks. In this work, we propose a deep dueling on-policy learning method (2DSARSA) for coordinated TSC for a network of intersections that maximizes the network throughput and minimizes the average end-to-end delay. To describe the states of the environment, we propose traffic flow maps (TFMs) that capture head-of-the-line (HOL) sojourn times for traffic lanes and HOL differences for adjacent intersections. We introduce a reward function defined by the power metric which is the ratio of the network throughput to the average end-to-end delay. The proposed reward function simultaneously maximizes the network throughput and minimizes the average end-to-end delay. We show that the proposed 2DSARSA architecture has a significantly better learning performance compared to other RL architectures including Deep Q-Network (DQN) and Deep SARSA (DSARSA).


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Deep On-Policy Learning Agent for Traffic Signal Control of Multiple Intersections


    Contributors:


    Publication date :

    2020-09-20


    Size :

    804051 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Deep Learning Agent for Traffic Signal Control

    Chitti, T N / Sanapala, Praneeth / Punith, C et al. | IEEE | 2024


    Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning

    Duowei Li / Jianping Wu / Ming Xu et al. | DOAJ | 2020

    Free access


    Adaptive Urban Traffic Signal Control for Multiple Intersections: An LQR Approach

    Park, Jiho / Liu, Tong / Wang, Chieh et al. | IEEE | 2022