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Cited by 62Traffic control and managementReinforcement Learning in RoboticsTraffic Prediction and Management Techniques

Multi-Agent Reinforcement Learning for Traffic Flow Management of Autonomous Vehicles

Open access
Mushtaq, Anum, Haq, Irfan Ul, Sarwar, Muhammad Azeem, Khan, Asifullah, Khalil, Wajeeha, Mughal, Muhammad Abid·2023·Sensors
DOI 10.3390/s23052373PMC PMC10007156
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Abstract

Intelligent traffic management systems have become one of the main applications of Intelligent Transportation Systems (ITS). There is a growing interest in Reinforcement Learning (RL) based control methods in ITS applications such as autonomous driving and traffic management solutions. Deep learning helps in approximating substantially complex nonlinear functions from complicated data sets and tackling complex control issues. In this paper, we propose an approach based on Multi-Agent Reinforcement Learning (MARL) and smart routing to improve the flow of autonomous vehicles on road networks. We evaluate Multi-Agent Advantage Actor-Critic (MA2C) and Independent Advantage Actor-Critical (IA2C), recently suggested Multi-Agent Reinforcement Learning techniques with smart routing for traffic signal optimization to determine its potential. We investigate the framework offered by non-Markov decision processes, enabling a more in-depth understanding of the algorithms. We conduct a critical analysis to observe the robustness and effectiveness of the method. The method’s efficacy and reliability are demonstrated by simulations using SUMO, a software modeling tool for traffic simulations. We used a road network that contains seven intersections. Our findings show that MA2C, when trained on pseudo-random vehicle flows, is a viable methodology that outperforms competing techniques.

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  • González Pacheco et al. [20] presents a secure ML pipeline for near-real-time control using distributed agents and DLT in 6G networks with limited attention to traffic signal coordination in urban VANET environments.

    “While the study presents a secure ML pipeline for near-real-time control using distributed agents and DLT in 6G networks with limited attention to traffic signal coordination in urban VANET environments.”

    From Multi-agent Reinforcement Learning for Real-time Traffic Control in 6G-Enabled VANET Environments (2025) · cites this paper as [20]

    The Proposed Methodology (section 3; page unavailable) says the paper focuses on controlling traffic signals with multi-agent reinforcement learning; its MA2C agents interact with neighboring agents when switching signals. That contradicts the claim that traffic-signal coordination receives limited attention. The cited paper is by Mushtaq et al., not González Pacheco et al.; the passages do not describe a secure DLT pipeline in 6G networks. (LLM, conf=0.87)

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Research works printed but not linked: 9

  1. [8]Mushtaq A. Sarwar M.A. Khan A. Shafiq O. Traffic Management of Autonomous Vehicles Using Policy Based Deep Reinforcement Learning and Intelligent Routing arXiv 2022 2206.14608
  2. [12]Genders W. Razavi S. Using a deep reinforcement learning agent for traffic signal control arXiv 2016 1611.01142
  3. [13]Bakker B. Whiteson S. Kester L. Groen F.C. Traffic light control by multiagent reinforcement learning systems Interactive Collaborative Information Systems Springer Berlin/Heidelberg, Germany 2010 475 510
  4. [14]Casas N. Deep deterministic policy gradient for urban traffic light control arXiv 2017 1703.09035
  5. [17]Zhang K. Yang Z. Başar T. Multi-Agent Reinforcement Learning: A selective overview of theories and algorithms Handbook of Reinforcement Learning and Control Springer Cham, Switzerland 2021 321 384
  6. [18]Buşoniu L. Babuška R. De Schutter B. Multi-Agent Reinforcement Learning: An overview Innovations in Multi-Agent Systems and Applications-1 Springer Berlin/Heidelberg, Germany 2010 183 221
  7. [24]Lanctot M. Zambaldi V. Gruslys A. Lazaridou A. Tuyls K. Pérolat J. Silver D. Graepel T. A unified game-theoretic approach to multiagent reinforcement learning arXiv 2017 1711.00832
  8. [25]Foerster J.N. Assael Y.M. De Freitas N. Whiteson S. Learning to communicate with deep Multi-Agent Reinforcement Learning arXiv 2016 1605.06676
  9. [35]Krajzewicz D. Traffic simulation with SUMO–simulation of urban mobility Fundamentals of Traffic Simulation Springer New York, NY, USA 2010 269 293

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Authors
Mushtaq, A., Haq, I. U., Sarwar, M. A., Khan, A., Khalil, W., & Mughal, M. A.
Affiliations
  • Pakistan Institute of Engineering and Applied Sciences (PK)
  • University of Engineering and Technology Peshawar (PK)
Corresponding author
Haq, Irfan Ul
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2023-02-21
Volume
23
Issue
5
Pages
2373
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s23052373
PMC
PMC10007156
OpenAlex
W4321497781
Cited by
61
References
29
Citations by year
2026: 18 · 2025: 23 · 2024: 16 · 2023: 4
FWCI
6.55
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
  • Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
  • Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
Keywords
multi-agent reinforcement learning, traffic flow management, autonomous vehicles, traffic signal optimization, smart routing, intelligent transportation systems, SUMO simulation

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