[{"citation":{"ieee":"A. Müller et al., Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control. [Piscataway, NJ]: IEEE, 2022, pp. 507–514. doi: 10.1109/icmla52953.2021.00085.","ama":"Müller A, Rangras V, Ferfers T, et al. Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control. (Wani MA, Sethi I, Shi W, et al., eds.). IEEE; 2022:507-514. doi:10.1109/icmla52953.2021.00085","chicago":"Müller, Arthur, Vishal Rangras, Tobias Ferfers, Florian Hufen, Lukas Schreckenberg, Jürgen Jasperneite, Georg Schnittker, Michael Waldmann, Maxim Friesen, and Marco Wiering. Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control. Edited by M. Arif Wani, Ishwar Sethi, Weisong Shi, Guangzhi Qu, Daniela Stan Raicu, Ruoming Jin, IEEE ICMLA , and Institute of Electrical and Electronics Engineers. 20th IEEE International Conference on Machine Learning and Applications (ICMLA). [Piscataway, NJ]: IEEE, 2022. https://doi.org/10.1109/icmla52953.2021.00085.","apa":"Müller, A., Rangras, V., Ferfers, T., Hufen, F., Schreckenberg, L., Jasperneite, J., Schnittker, G., Waldmann, M., Friesen, M., & Wiering, M. (2022). Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control. In M. A. Wani, I. Sethi, W. Shi, G. Qu, D. Stan Raicu, R. Jin, IEEE ICMLA , & Institute of Electrical and Electronics Engineers (Eds.), 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 507–514). IEEE. https://doi.org/10.1109/icmla52953.2021.00085","mla":"Müller, Arthur, et al. “Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control.” 20th IEEE International Conference on Machine Learning and Applications (ICMLA), edited by M. Arif Wani et al., IEEE, 2022, pp. 507–14, https://doi.org/10.1109/icmla52953.2021.00085.","havard":"A. Müller, V. Rangras, T. Ferfers, F. Hufen, L. Schreckenberg, J. Jasperneite, G. Schnittker, M. Waldmann, M. Friesen, M. Wiering, Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control, IEEE, [Piscataway, NJ], 2022.","bjps":"Müller A et al. (2022) Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control, Wani MA et al. (eds). [Piscataway, NJ]: IEEE.","ufg":"Müller, Arthur u. a.: Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control, hg. von Wani, M. Arif u. a., [Piscataway, NJ] 2022.","van":"Müller A, Rangras V, Ferfers T, Hufen F, Schreckenberg L, Jasperneite J, et al. Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control. Wani MA, Sethi I, Shi W, Qu G, Stan Raicu D, Jin R, et al., editors. 20th IEEE International Conference on Machine Learning and Applications (ICMLA). [Piscataway, NJ]: IEEE; 2022.","chicago-de":"Müller, Arthur, Vishal Rangras, Tobias Ferfers, Florian Hufen, Lukas Schreckenberg, Jürgen Jasperneite, Georg Schnittker, Michael Waldmann, Maxim Friesen und Marco Wiering. 2022. Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control. Hg. von M. Arif Wani, Ishwar Sethi, Weisong Shi, Guangzhi Qu, Daniela Stan Raicu, Ruoming Jin, IEEE ICMLA , und Institute of Electrical and Electronics Engineers. 20th IEEE International Conference on Machine Learning and Applications (ICMLA). [Piscataway, NJ]: IEEE. doi:10.1109/icmla52953.2021.00085, .","din1505-2-1":"Müller, Arthur ; Rangras, Vishal ; Ferfers, Tobias ; Hufen, Florian ; Schreckenberg, Lukas ; Jasperneite, Jürgen ; Schnittker, Georg ; Waldmann, Michael ; u. a. ; Wani, M. A. ; Sethi, I. ; Shi, W. ; Qu, G. ; Stan Raicu, D. ; Jin, R. ; IEEE ICMLA ; Institute of Electrical and Electronics Engineers (Hrsg.): Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control. [Piscataway, NJ] : IEEE, 2022","short":"A. Müller, V. Rangras, T. Ferfers, F. Hufen, L. Schreckenberg, J. Jasperneite, G. Schnittker, M. Waldmann, M. Friesen, M. Wiering, Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control, IEEE, [Piscataway, NJ], 2022."},"title":"Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control","status":"public","page":"507-514","year":"2022","author":[{"last_name":"Müller","first_name":"Arthur","full_name":"Müller, Arthur"},{"last_name":"Rangras","first_name":"Vishal","id":"76044","full_name":"Rangras, Vishal"},{"last_name":"Ferfers","first_name":"Tobias","full_name":"Ferfers, Tobias"},{"last_name":"Hufen","first_name":"Florian","full_name":"Hufen, Florian"},{"full_name":"Schreckenberg, Lukas","first_name":"Lukas","last_name":"Schreckenberg"},{"last_name":"Jasperneite","first_name":"Jürgen","full_name":"Jasperneite, Jürgen","id":"1899"},{"last_name":"Schnittker","full_name":"Schnittker, Georg","first_name":"Georg"},{"last_name":"Waldmann","full_name":"Waldmann, Michael","first_name":"Michael"},{"id":"61517","full_name":"Friesen, Maxim","first_name":"Maxim","last_name":"Friesen"},{"full_name":"Wiering, Marco","first_name":"Marco","last_name":"Wiering"}],"corporate_editor":[" IEEE ICMLA ","Institute of Electrical and Electronics Engineers"],"editor":[{"first_name":"M. Arif ","last_name":"Wani","full_name":"Wani, M. Arif "},{"first_name":"Ishwar ","last_name":"Sethi","full_name":"Sethi, Ishwar "},{"first_name":"Weisong","last_name":" Shi","full_name":" Shi, Weisong"},{"first_name":"Guangzhi ","full_name":"Qu, Guangzhi ","last_name":"Qu"},{"full_name":"Stan Raicu, Daniela ","last_name":"Stan Raicu","first_name":"Daniela "},{"last_name":"Jin","full_name":"Jin, Ruoming ","first_name":"Ruoming "}],"date_updated":"2025-06-26T13:28:21Z","publication_identifier":{"isbn":["978-1-6654-4337-1"]},"publication":"20th IEEE International Conference on Machine Learning and Applications (ICMLA)","date_created":"2025-04-17T08:45:40Z","keyword":["deep reinforcement learning","traffic signal control","intelligent transportation system","traffic simulation"],"language":[{"iso":"eng"}],"publication_status":"published","publisher":"IEEE","abstract":[{"lang":"eng","text":"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."}],"conference":{"start_date":"2021-12-13","end_date":"2021-12-16","location":"Online","name":"20th IEEE International Conference on Machine Learning and Applications (ICMLA)"},"department":[{"_id":"DEP5023"}],"user_id":"83781","type":"conference_editor_article","_id":"12817","doi":"10.1109/icmla52953.2021.00085","place":"[Piscataway, NJ]"},{"publication_identifier":{"eisbn":["9781665443371"]},"publication":"20th IEEE International Conference on Machine Learning and Applications (ICMLA)","date_created":"2024-07-30T05:54:40Z","date_updated":"2024-07-30T07:45:47Z","corporate_editor":[" IEEE ICMLA"," Institute of Electrical and Electronics Engineers"],"editor":[{"first_name":"M. Arif","last_name":"Wani","full_name":"Wani, M. Arif"}],"keyword":["deep reinforcement learning","traffic signal control","intelligent transportation system","traffic simulation"],"language":[{"iso":"eng"}],"title":"Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control","citation":{"apa":"Müller, A., Rangras, V., Schnittker, G., Waldmann, M., Friesen, M., Ferfers, T., Schreckenberg, L., Hufen, F., Jasperneite, J., & Wiering, M. (2021). Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control. In M. A. Wani, IEEE ICMLA, & Institute of Electrical and Electronics Engineers (Eds.), 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE. https://doi.org/10.1109/ICMLA52953.2021.00085","chicago":"Müller, Arthur, Vishal Rangras, Georg Schnittker, Michael Waldmann, Maxim Friesen, Tobias Ferfers, Lukas Schreckenberg, Florian Hufen, Jürgen Jasperneite, and Marco Wiering. Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control. Edited by M. Arif Wani, IEEE ICMLA, and Institute of Electrical and Electronics Engineers. 20th IEEE International Conference on Machine Learning and Applications (ICMLA). Piscataway, NJ: IEEE, 2021. https://doi.org/10.1109/ICMLA52953.2021.00085.","van":"Müller A, Rangras V, Schnittker G, Waldmann M, Friesen M, Ferfers T, et al. Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control. Wani MA, IEEE ICMLA, Institute of Electrical and Electronics Engineers, editors. 20th IEEE International Conference on Machine Learning and Applications (ICMLA). Piscataway, NJ: IEEE; 2021.","ufg":"Müller, Arthur u. a.: Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control, hg. von Wani, M. Arif/ IEEE ICMLA, Institute of Electrical and Electronics Engineers, Piscataway, NJ 2021.","bjps":"Müller A et al. (2021) Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control, Wani MA, IEEE ICMLA, and Institute of Electrical and Electronics Engineers (eds). Piscataway, NJ: IEEE.","mla":"Müller, Arthur, et al. “Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control.” 20th IEEE International Conference on Machine Learning and Applications (ICMLA), edited by M. Arif Wani et al., IEEE, 2021, https://doi.org/10.1109/ICMLA52953.2021.00085.","havard":"A. Müller, V. Rangras, G. Schnittker, M. Waldmann, M. Friesen, T. Ferfers, L. Schreckenberg, F. Hufen, J. Jasperneite, M. Wiering, Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control, IEEE, Piscataway, NJ, 2021.","short":"A. Müller, V. Rangras, G. Schnittker, M. Waldmann, M. Friesen, T. Ferfers, L. Schreckenberg, F. Hufen, J. Jasperneite, M. Wiering, Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control, IEEE, Piscataway, NJ, 2021.","chicago-de":"Müller, Arthur, Vishal Rangras, Georg Schnittker, Michael Waldmann, Maxim Friesen, Tobias Ferfers, Lukas Schreckenberg, Florian Hufen, Jürgen Jasperneite und Marco Wiering. 2021. Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control. Hg. von M. Arif Wani, IEEE ICMLA, und Institute of Electrical and Electronics Engineers. 20th IEEE International Conference on Machine Learning and Applications (ICMLA). Piscataway, NJ: IEEE. doi:10.1109/ICMLA52953.2021.00085, .","din1505-2-1":"Müller, Arthur ; Rangras, Vishal ; Schnittker, Georg ; Waldmann, Michael ; Friesen, Maxim ; Ferfers, Tobias ; Schreckenberg, Lukas ; Hufen, Florian ; u. a. ; Wani, M. A. ; IEEE ICMLA ; Institute of Electrical and Electronics Engineers (Hrsg.): Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control. Piscataway, NJ : IEEE, 2021","ieee":"A. Müller et al., Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control. Piscataway, NJ: IEEE, 2021. doi: 10.1109/ICMLA52953.2021.00085.","ama":"Müller A, Rangras V, Schnittker G, et al. Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control. (Wani MA, IEEE ICMLA, Institute of Electrical and Electronics Engineers, eds.). IEEE; 2021. doi:10.1109/ICMLA52953.2021.00085"},"author":[{"first_name":"Arthur","full_name":"Müller, Arthur","last_name":"Müller"},{"full_name":"Rangras, Vishal","first_name":"Vishal","id":"76044","last_name":"Rangras"},{"last_name":"Schnittker","first_name":"Georg","full_name":"Schnittker, Georg"},{"first_name":"Michael","full_name":"Waldmann, Michael","last_name":"Waldmann"},{"id":"61517","full_name":"Friesen, Maxim","first_name":"Maxim","last_name":"Friesen"},{"first_name":"Tobias","full_name":"Ferfers, Tobias","last_name":"Ferfers"},{"full_name":"Schreckenberg, Lukas","first_name":"Lukas","last_name":"Schreckenberg"},{"last_name":"Hufen","full_name":"Hufen, Florian","first_name":"Florian"},{"full_name":"Jasperneite, Jürgen","id":"1899","last_name":"Jasperneite","first_name":"Jürgen"},{"last_name":"Wiering","full_name":"Wiering, Marco","first_name":"Marco"}],"year":"2021","external_id":{"arxiv":["arXiv:2103.16223"]},"status":"public","type":"conference_editor_article","user_id":"83781","place":"Piscataway, NJ","_id":"11803","doi":"10.1109/ICMLA52953.2021.00085","publication_status":"published","conference":{"location":"Pasadena, CA, USA ","name":"20th IEEE International Conference on Machine Learning and Applications (ICMLA)","start_date":"2021-12-13","end_date":"2021-12-16"},"department":[{"_id":"DEP5000"},{"_id":"DEP5019"},{"_id":"DEP5020"},{"_id":"DEP6020"}],"abstract":[{"lang":"eng","text":"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."}],"publisher":"IEEE"}]