IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i4p3479-d1067905.html
   My bibliography  Save this article

Multi-Agent Reinforcement Learning for Traffic Signal Control: A Cooperative Approach

Author

Listed:
  • Máté Kolat

    (Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, H-1111 Budapest, Hungary)

  • Bálint Kővári

    (Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, H-1111 Budapest, Hungary)

  • Tamás Bécsi

    (Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, H-1111 Budapest, Hungary)

  • Szilárd Aradi

    (Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, H-1111 Budapest, Hungary)

Abstract

The rapid growth of urbanization and the constant demand for mobility have put a great strain on transportation systems in cities. One of the major challenges in these areas is traffic congestion, particularly at signalized intersections. This problem not only leads to longer travel times for commuters, but also results in a significant increase in local and global emissions. The fixed cycle of traffic lights at these intersections is one of the primary reasons for this issue. To address these challenges, applying reinforcement learning to coordinating traffic light controllers has become a highly researched topic in the field of transportation engineering. This paper focuses on the traffic signal control problem, proposing a solution using a multi-agent deep Q-learning algorithm. This study introduces a novel rewarding concept in the multi-agent environment, as the reward schemes have yet to evolve in the following years with the advancement of techniques. The goal of this study is to manage traffic networks in a more efficient manner, taking into account both sustainability and classic measures. The results of this study indicate that the proposed approach can bring about significant improvements in transportation systems. For instance, the proposed approach can reduce fuel consumption by 11% and average travel time by 13%. The results of this study demonstrate the potential of reinforcement learning in improving the coordination of traffic light controllers and reducing the negative impacts of traffic congestion in urban areas. The implementation of this proposed solution could contribute to a more sustainable and efficient transportation system in the future.

Suggested Citation

  • Máté Kolat & Bálint Kővári & Tamás Bécsi & Szilárd Aradi, 2023. "Multi-Agent Reinforcement Learning for Traffic Signal Control: A Cooperative Approach," Sustainability, MDPI, vol. 15(4), pages 1-13, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3479-:d:1067905
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/4/3479/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/4/3479/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    2. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wenjiao Zai & Dan Yang, 2023. "Improved Deep Reinforcement Learning for Intelligent Traffic Signal Control Using ECA_LSTM Network," Sustainability, MDPI, vol. 15(18), pages 1-23, September.
    2. Anna Górka & Andrzej Czerepicki & Tomasz Krukowicz, 2024. "The Impact of Priority in Coordinated Traffic Lights on Tram Energy Consumption," Energies, MDPI, vol. 17(2), pages 1-24, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yassine Chemingui & Adel Gastli & Omar Ellabban, 2020. "Reinforcement Learning-Based School Energy Management System," Energies, MDPI, vol. 13(23), pages 1-21, December.
    2. Yuhong Wang & Lei Chen & Hong Zhou & Xu Zhou & Zongsheng Zheng & Qi Zeng & Li Jiang & Liang Lu, 2021. "Flexible Transmission Network Expansion Planning Based on DQN Algorithm," Energies, MDPI, vol. 14(7), pages 1-21, April.
    3. Huang, Ruchen & He, Hongwen & Gao, Miaojue, 2023. "Training-efficient and cost-optimal energy management for fuel cell hybrid electric bus based on a novel distributed deep reinforcement learning framework," Applied Energy, Elsevier, vol. 346(C).
    4. Neha Soni & Enakshi Khular Sharma & Narotam Singh & Amita Kapoor, 2019. "Impact of Artificial Intelligence on Businesses: from Research, Innovation, Market Deployment to Future Shifts in Business Models," Papers 1905.02092, arXiv.org.
    5. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    6. Taejong Joo & Hyunyoung Jun & Dongmin Shin, 2022. "Task Allocation in Human–Machine Manufacturing Systems Using Deep Reinforcement Learning," Sustainability, MDPI, vol. 14(4), pages 1-18, February.
    7. Mahmoud Mahfouz & Tucker Balch & Manuela Veloso & Danilo Mandic, 2021. "Learning to Classify and Imitate Trading Agents in Continuous Double Auction Markets," Papers 2110.01325, arXiv.org, revised Oct 2021.
    8. Oleh Lukianykhin & Tetiana Bogodorova, 2021. "Voltage Control-Based Ancillary Service Using Deep Reinforcement Learning," Energies, MDPI, vol. 14(8), pages 1-22, April.
    9. Wu, Yuankai & Tan, Huachun & Peng, Jiankun & Zhang, Hailong & He, Hongwen, 2019. "Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 247(C), pages 454-466.
    10. Chen, Jiaxin & Shu, Hong & Tang, Xiaolin & Liu, Teng & Wang, Weida, 2022. "Deep reinforcement learning-based multi-objective control of hybrid power system combined with road recognition under time-varying environment," Energy, Elsevier, vol. 239(PC).
    11. Amirhosein Mosavi & Yaser Faghan & Pedram Ghamisi & Puhong Duan & Sina Faizollahzadeh Ardabili & Ely Salwana & Shahab S. Band, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Mathematics, MDPI, vol. 8(10), pages 1-42, September.
    12. Zhang, Yihao & Chai, Zhaojie & Lykotrafitis, George, 2021. "Deep reinforcement learning with a particle dynamics environment applied to emergency evacuation of a room with obstacles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    13. Alessio Brini & Daniele Tantari, 2021. "Deep Reinforcement Trading with Predictable Returns," Papers 2104.14683, arXiv.org, revised May 2023.
    14. Georgios D. Kontes & Georgios I. Giannakis & Víctor Sánchez & Pablo De Agustin-Camacho & Ander Romero-Amorrortu & Natalia Panagiotidou & Dimitrios V. Rovas & Simone Steiger & Christopher Mutschler & G, 2018. "Simulation-Based Evaluation and Optimization of Control Strategies in Buildings," Energies, MDPI, vol. 11(12), pages 1-23, December.
    15. Weifan Long & Taixian Hou & Xiaoyi Wei & Shichao Yan & Peng Zhai & Lihua Zhang, 2023. "A Survey on Population-Based Deep Reinforcement Learning," Mathematics, MDPI, vol. 11(10), pages 1-17, May.
    16. Chanjuan Liu & Jinmiao Cong & Tianhao Zhao & Enqiang Zhu, 2023. "Improving Agent Decision Payoffs via a New Framework of Opponent Modeling," Mathematics, MDPI, vol. 11(14), pages 1-15, July.
    17. Yifeng Guo & Xingyu Fu & Yuyan Shi & Mingwen Liu, 2018. "Robust Log-Optimal Strategy with Reinforcement Learning," Papers 1805.00205, arXiv.org.
    18. Guan, Xiaoshu & Xiang, Zhengliang & Bao, Yuequan & Li, Hui, 2022. "Structural dominant failure modes searching method based on deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    19. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
    20. Yuchao Dong, 2022. "Randomized Optimal Stopping Problem in Continuous time and Reinforcement Learning Algorithm," Papers 2208.02409, arXiv.org, revised Sep 2023.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3479-:d:1067905. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.