The DOI matches its Crossref record; 26 of the 27 references it cites were found in a registry or an index.
Multi-Agent Reinforcement Learning for Traffic Signal Control: A Cooperative Approach
Open accessAbstract
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.
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“Nevertheless, there are still difficulties in scaling the approach to different traffic situations like congestion timing and modifying reward mechanisms to such situations.”
From Multi-agent Reinforcement Learning for Real-time Traffic Control in 6G-Enabled VANET Environments (2025) · cites this paper as [18]
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Cites
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
(1999) · Cited by 5,147
End-to-End Training of Deep Visuomotor PoliciesPreprint
Levine, S., Finn, C., Darrell, T., & Abbeel, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.00702 · Cited by 2,643
Temporal difference learning and TD-Gammon
Tesauro, G. (1995) · Communications of the ACM · doi:10.1145/203330.203343 · Cited by 1,618
Target-driven visual navigation in indoor scenes using deep reinforcement learning
Zhu, Y., Mottaghi, R., Kolve, E., Lim, J. J., Gupta, A., Fei-Fei, L., & Farhadi, A. (2017) · 2017 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2017.7989381 · Cited by 1,533
The dynamics of reinforcement learning in cooperative multiagent systems
Claus, C., & Boutilier, C. E. (1998) · Cited by 1,094
Wei, H., Zheng, G., Yao, H., & Li, Z. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220096 · Cited by 697
Policy gradient reinforcement learning for fast quadrupedal locomotion
Kohl, N., & Stone, P. (2004) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2004.1307456 · Cited by 586
Psychological Research on Joint Action
Knoblich, G., Butterfill, S., & Sebanz, N. (2011) · The Psychology of learning and motivation/The psychology of learning and motivation · doi:10.1016/b978-0-12-385527-5.00003-6 · Cited by 577
Research works printed but not linked: 7
- Roess, R.P., Prassas, E.S., and McShane, W.R. (2004). Traffic Engineering, Pearson/Prentice Hall.
- Varaiya, P. (2013). Advances in Dynamic Network Modeling in Complex Transportation Systems, Springer.
- Van der Pol, E., and Oliehoek, F.A. (2016, January 10). Coordinated deep reinforcement learners for traffic light control. Proceedings of the Learning, Inference and Control of Multi-Agent Systems (at NIPS 2016), Barcelona, Spain.
- Genders, W., and Razavi, S. (2016). Using a deep reinforcement learning agent for traffic signal control. arXiv.
- Ng, A.Y., Coates, A., Diel, M., Ganapathi, V., Schulte, J., Tse, B., Berger, E., and Liang, E. (2006). Experimental Robotics IX, Springer.
- Nowé, A., Vrancx, P., and Hauwere, Y.M.D. (2012). Reinforcement Learning, Springer.
- Nair, A., Srinivasan, P., Blackwell, S., Alcicek, C., Fearon, R., De Maria, A., Panneershelvam, V., Suleyman, M., Beattie, C., and Petersen, S. (2015). Massively parallel methods for deep reinforcement learning. arXiv.
Cited by
Sorted by citations
Traffic Signal Control via Reinforcement Learning: A Review on Applications and Innovations
Michailidis, P., Michailidis, I., Lazaridis, C. R., & Kosmatopoulos, E. B. (2025) · Infrastructures · doi:10.3390/infrastructures10050114 · Cited by 129
Artificial Intelligence-Based Adaptive Traffic Signal Control System: A Comprehensive Review
Agrahari, A., Dhabu, M. M., Deshpande, P. S., Tiwari, A., Baig, M. A., & Sawarkar, A. D. (2024) · Electronics · doi:10.3390/electronics13193875 · Cited by 103
Saadi, A., Abghour, N., Chiba, Z., Moussaid, K., & Ali, S. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01104-x · Cited by 50
A traffic light control method based on multi-agent deep reinforcement learning algorithm
Liu, D., & Li, L. (2023) · Scientific Reports · doi:10.1038/s41598-023-36606-2 · Cited by 35
Hussain, Q., Noor, A. S. M., Qureshi, M. M., Li, J., Rahman, A., Bakry, A., Mahmood, T., & Rehman, A. U. (2025) · Scientific Reports · doi:10.1038/s41598-025-86608-5 · Cited by 33
Metadata
- Authors
- Kolat, M., Kővári, B., Bécsi, T., & Aradi, S.
- Affiliations
- Budapest University of Technology and Economics (HU)
- Corresponding author
- Bécsi, Tamás
- Aradi, Szilárd
- Venue
- Sustainability
- Publisher
- MDPI AG
- Published
- 2023-02-14
- Volume
- 15
- Issue
- 4
- Pages
- 3479
- ISSN
- 2071-1050 (ISSN-L)
- OpenAlex
- W4320729567
- Cited by
- 94
- References
- 27
- Citations by year
- 2026: 33 · 2025: 36 · 2024: 21 · 2023: 4
- FWCI
- 10.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sustainability (publishedVersion, cc-by)
- Repository of the Academy's Library (Library of the Hungarian Academy of Sciences) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- European Union (RRF-2.3.1-21-2022-00002, BME-NVA-02)
- National Research, Development and Innovation Fund (RRF-2.3.1-21-2022-00002, BME-NVA-02)
- János Bolyai Research Scholarship of the Hungarian Academy of Sciences (RRF-2.3.1-21-2022-00002, BME-NVA-02)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Social Sciences › Transportation › Traffic Prediction and Management Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- traffic signal control, traffic congestion, signalized intersections, reward function design, fuel consumption reduction, travel time reduction, sustainable transport, urban traffic management
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