Expanded Applicability: Multi-Agent Reinforcement Learning-Based Traffic Signal Control in a Variable-Sized Environment

István Knáb, Bálint Pelenczei, Bálint Kővári, Bálint Kővári, Tamás Bécsi, László Palkovics, László Palkovics

2024

Abstract

During the development of modern cities, there is a strong demand articulated for the sustainability of progress. Since transportation is one of the main contributors to greenhouse gas emissions, the modernization and efficiency of transportation are key issues in the development of livable cities. Increasing the number of lanes does not always provide a solution and often is not feasible for various reasons. In such cases, Intelligent Transportation Systems are applied primarily in urban environments, mostly in the form of Traffic Signal Control. The majority of modern cities already employ adaptive traffic signals, but these largely utilize rule-based algorithms. Due to the stochastic nature of traffic, there arises a demand for cognitive decision-making that enables event-driven characteristics with the assistance of machine learning algorithms. While there are existing solutions utilizing Reinforcement Learning to address the problem, further advancements can be achieved in various areas. This paper presents a solution that not only reduces emissions and enhances network throughput but also ensures universal applicability regardless of network size, owing to individually tailored state representation and rewards.

Paper Citation

in Harvard Style

Knáb I., Pelenczei B., Kővári B., Bécsi T. and Palkovics L. (2024). Expanded Applicability: Multi-Agent Reinforcement Learning-Based Traffic Signal Control in a Variable-Sized Environment. In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO; ISBN 978-989-758-717-7, SciTePress, pages 15-25. DOI: 10.5220/0012920800003822

in Bibtex Style

@conference{icinco24,
author={István Knáb and Bálint Pelenczei and Bálint Kővári and Tamás Bécsi and László Palkovics},
title={Expanded Applicability: Multi-Agent Reinforcement Learning-Based Traffic Signal Control in a Variable-Sized Environment},
booktitle={Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
year={2024},
pages={15-25},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012920800003822},
isbn={978-989-758-717-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO
TI - Expanded Applicability: Multi-Agent Reinforcement Learning-Based Traffic Signal Control in a Variable-Sized Environment
SN - 978-989-758-717-7
AU - Knáb I.
AU - Pelenczei B.
AU - Kővári B.
AU - Bécsi T.
AU - Palkovics L.
PY - 2024
SP - 15
EP - 25
DO - 10.5220/0012920800003822
PB - SciTePress