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  4. A Reinforcement Learning Approach for Traffic Control
 
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2021
  • Konferenzbeitrag

Titel

A Reinforcement Learning Approach for Traffic Control

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.
Author(s)
Baumgart, Urs
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM
Burger, Michael
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM
Hauptwerk
7th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2021. Proceedings
Konferenz
International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS) 2021
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DOI
10.5220/0010448501330141
Language
Englisch
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