---
res:
  bibo_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. To demonstrate the functionality and applicability of LemgoRL, we train
    a state-of-the-art Deep RL algorithm on a CPU cluster utilizing a framework for
    distributed and parallel RL and compare its performance with other methods. Our
    benchmark tool drives the development of RL algorithms towards real-world applications.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Arthur
      foaf_name: Müller, Arthur
      foaf_surname: Müller
  - foaf_Person:
      foaf_givenName: Vishal
      foaf_name: Rangras, Vishal
      foaf_surname: Rangras
      foaf_workInfoHomepage: http://www.librecat.org/personId=76044
  - foaf_Person:
      foaf_givenName: Tobias
      foaf_name: Ferfers, Tobias
      foaf_surname: Ferfers
  - foaf_Person:
      foaf_givenName: Florian
      foaf_name: Hufen, Florian
      foaf_surname: Hufen
  - foaf_Person:
      foaf_givenName: Lukas
      foaf_name: Schreckenberg, Lukas
      foaf_surname: Schreckenberg
  - foaf_Person:
      foaf_givenName: Jürgen
      foaf_name: Jasperneite, Jürgen
      foaf_surname: Jasperneite
      foaf_workInfoHomepage: http://www.librecat.org/personId=1899
  - foaf_Person:
      foaf_givenName: Georg
      foaf_name: Schnittker, Georg
      foaf_surname: Schnittker
  - foaf_Person:
      foaf_givenName: Michael
      foaf_name: Waldmann, Michael
      foaf_surname: Waldmann
  - foaf_Person:
      foaf_givenName: Maxim
      foaf_name: Friesen, Maxim
      foaf_surname: Friesen
      foaf_workInfoHomepage: http://www.librecat.org/personId=61517
  - foaf_Person:
      foaf_givenName: Marco
      foaf_name: Wiering, Marco
      foaf_surname: Wiering
  bibo_doi: 10.1109/icmla52953.2021.00085
  dct_date: 2022^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/978-1-6654-4337-1
  dct_language: eng
  dct_publisher: IEEE@
  dct_subject:
  - deep reinforcement learning
  - traffic signal control
  - intelligent transportation system
  - traffic simulation
  dct_title: Towards Real-World Deployment of Reinforcement Learning for Traffic Signal
    Control@
...