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LemgoRL: An open-source Benchmark Tool to Train Reinforcement Learning Agents for Traffic Signal Control in a real-world simulation scenario.
Arthur Müller,Vishal Rangras,Georg Schnittker,Michael Waldmann,Maxim Friesen,Tobias Ferfers,Lukas Schreckenberg,Florian Hufen,Jurgen Jasperneite,Marco A. Wiering +9 more
TLDR
LemgoRL as mentioned in this paper is a benchmark tool to train RL agents as TSC in a realistic simulation environment of Lemgo, a medium-sized town in Germany, which includes a traffic signal logic unit that ensures compliance with all regulatory and safety requirements.Abstract:
Sub-optimal control policies in intersection traffic signal controllers (TSC) contribute to congestion and lead to negative effects on human health and the environment. Reinforcement learning (RL) for traffic signal control is a promising approach to design better control policies and has attracted considerable research interest in recent years. However, most work done in this area used simplified simulation environments of traffic scenarios to train RL-based TSC. To deploy RL in real-world traffic systems, the gap between simplified simulation environments and real-world applications has to be closed. Therefore, we propose LemgoRL, a benchmark tool to train RL agents as TSC in a realistic simulation environment of Lemgo, a medium-sized town in Germany. In addition to the realistic simulation model, LemgoRL encompasses a traffic signal logic unit that ensures compliance with all regulatory and safety requirements. LemgoRL offers the same interface as the well-known OpenAI gym toolkit to enable easy deployment in existing research work. Our benchmark tool drives the development of RL algorithms towards real-world applications. We provide LemgoRL as an open-source tool at this https URL.read more
References
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Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Journal ArticleDOI
Mastering the game of Go without human knowledge
David Silver,Julian Schrittwieser,Karen Simonyan,Ioannis Antonoglou,Aja Huang,Arthur Guez,Thomas Hubert,Lucas Baker,Matthew Lai,Adrian Bolton,Yutian Chen,Timothy P. Lillicrap,Fan Hui,Laurent Sifre,George van den Driessche,Thore Graepel,Demis Hassabis +16 more
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Recent Development and Applications of SUMO - Simulation of Urban MObility
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Proceedings ArticleDOI
Microscopic Traffic Simulation using SUMO
Pablo Alvarez Lopez,Michael Behrisch,Laura Bieker-Walz,Jakob Erdmann,Yun-Pang Flötteröd,Robert Hilbrich,Leonhard Lücken,Johannes Rummel,Peter Wagner,Evamarie WieBner +9 more
TL;DR: The latest developments concerning intermodal traffic solutions, simulator coupling and model development and validation on the example of the open source traffic simulator SUMO are presented.
Microscopic modeling of traffic flow: investigation of collision free vehicle dynamics.
TL;DR: A microsopic model of traffic flow is proposed, adding to the understanding of the different types of congestion found in traffic flow, to find out how to optimize traffic with respect to a reduction of environmental impacts and economical loss due to congestion.