A Reinforcement Learning Approach for Traffic Control

Urs Baumgart, Michael Burger

Abstract

Intelligent traffic control is a key tool to achieve and to realize resource-efficient and sustainable mobility solutions. In this contribution, we study a promising data-based control approach, reinforcement learning (RL), and its applicability to traffic flow problems in a virtual environment. We model different traffic networks using the microscopic traffic simulation software SUMO. RL-methods are used to teach controllers, so called RL agents, to guide certain vehicles or to control a traffic light system. The agents obtain real-time information from other vehicles and learn to improve the traffic flow by repetitive observation and algorithmic optimization. As controller models, we consider both simple linear models and non-linear radial basis function networks. The latter allow to include prior knowledge from the training data and a two-step training procedure leading to an efficient controller training.

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Paper Citation


in Harvard Style

Baumgart U. and Burger M. (2021). A Reinforcement Learning Approach for Traffic Control. In Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-513-5, pages 133-141. DOI: 10.5220/0010448501330141


in Bibtex Style

@conference{vehits21,
author={Urs Baumgart and Michael Burger},
title={A Reinforcement Learning Approach for Traffic Control},
booktitle={Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2021},
pages={133-141},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010448501330141},
isbn={978-989-758-513-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - A Reinforcement Learning Approach for Traffic Control
SN - 978-989-758-513-5
AU - Baumgart U.
AU - Burger M.
PY - 2021
SP - 133
EP - 141
DO - 10.5220/0010448501330141