@misc{12817,
  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.}},
  author       = {{Müller, Arthur and Rangras, Vishal and Ferfers, Tobias and Hufen, Florian and Schreckenberg, Lukas and Jasperneite, Jürgen and Schnittker, Georg and Waldmann, Michael and Friesen, Maxim and Wiering, Marco}},
  booktitle    = {{20th IEEE International Conference on Machine Learning and Applications (ICMLA)}},
  editor       = {{Wani, M. Arif  and Sethi, Ishwar  and  Shi, Weisong and Qu, Guangzhi  and Stan Raicu, Daniela  and Jin, Ruoming }},
  isbn         = {{978-1-6654-4337-1}},
  keywords     = {{deep reinforcement learning, traffic signal control, intelligent transportation system, traffic simulation}},
  location     = {{Online}},
  pages        = {{507--514}},
  publisher    = {{IEEE}},
  title        = {{{Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control}}},
  doi          = {{10.1109/icmla52953.2021.00085}},
  year         = {{2022}},
}

@misc{11803,
  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.}},
  author       = {{Müller, Arthur and Rangras, Vishal and Schnittker, Georg and Waldmann, Michael and Friesen, Maxim and Ferfers, Tobias and Schreckenberg, Lukas and Hufen, Florian and Jasperneite, Jürgen and Wiering, Marco}},
  booktitle    = {{20th IEEE International Conference on Machine Learning and Applications (ICMLA)}},
  editor       = {{Wani, M. Arif}},
  keywords     = {{deep reinforcement learning, traffic signal control, intelligent transportation system, traffic simulation}},
  location     = {{Pasadena, CA, USA }},
  publisher    = {{IEEE}},
  title        = {{{Towards Real-World Deployment of Reinforcement Learning for Traffic  Signal Control}}},
  doi          = {{10.1109/ICMLA52953.2021.00085}},
  year         = {{2021}},
}