Chalaki, B., Beaver, L.E., Remer, B., Jang, K., Vinitsky, E., Bayen, A.M., et al., 2020. Zero-shot autonomous vehicle policy transfer: from simulation to real-world via adversarial learning. In: 2020 IEEE 16th International Conference on Control & Automation (ICCA). October 9-11, 2020, Singapore. IEEE, pp. 35–40.
Coşkun, M., Baggag, A., Chawla, S., 2018. Deep reinforcement learning for traffic light optimization. In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW). November 17-20, 2018, Singapore. IEEE, pp. 564–571.
Davarynejad, M., Hegyi, A., Vrancken, J., van den Berg, J., 2011. Motorway rampmetering control with queuing consideration using Q-learning. In: 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC). October 5-7, 2011. IEEE, Washington, DC, USA, pp. 1652–1658.
Duan, H., Li, Z., Zhang, Y., 2010. Multiobjective reinforcement learning for traffic signal control using vehicular ad hoc network. EURASIP J. Appl. Signal Process. 2010, 7, 1–7.
Fujimoto, S., Hoof, H., Meger, D., 2018, July. Addressing function approximation error in actor-critic methods. In: International conference on machine learning. PMLR, pp. 1587–1596.
Han, G., Han, Y., Wang, H., Ruan, T., Li, C., 2023. Coordinated control of urban expressway integrating adjacent signalized intersections using adversarial network based reinforcement learning method. In: IEEE Trans Intell Transp Syst, pp. 1–15.
Jang, K., Vinitsky, E., Chalaki, B., Remer, B., Beaver, L., Malikopoulos, A.A., et al., 2019. Simulation to scaled city: zero-shot policy transfer for traffic control via autonomous vehicles. In: Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems. April 16-18, 2019, Montreal, Quebec. ACM, Canada. New York, pp. 291–300.
Khamis, M.A., Gomaa, W., 2013. Enhanced multiagent multi-objective reinforcement learning for urban traffic light control. In: 2012 11th International Conference on Machine Learning and Applications. December 12-15, 2012. IEEE, Boca Raton, FL, USA, pp. 586–591.
Khamis, M.A., Gomaa, W., El-Shishiny, H., 2012. Multi-objective traffic light control system based on Bayesian probability interpretation. In: 2012 15th International IEEE Conference on Intelligent Transportation Systems. September 16-19, 2012. IEEE, Anchorage, AK, USA, pp. 995–1000.
Kreidieh, A.R., Wu, C., Bayen, A.M., 2018. Dissipating stop-and-go waves in closed and open networks via deep reinforcement learning. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC). November 4-7, 2018. IEEE, Maui, HI, USA, pp. 1475–1480.
Kunjir, M., Chawla, S., Chandrasekar, S., Jay, D., Ravindran, B., 2023. Optimizing traffic control with model-based learning: a pessimistic approach to data-efficient policy inference. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. August 6-10, 2023. ACM, Long Beach, CA, USA. New York, pp. 1176–1187.
Kuyer, L., Whiteson, S., Bakker, B., Vlassis, N., 2008. Multiagent reinforcement learning for urban traffic control using coordination graphs. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Berlin, Heidelberg, pp. 656–671.
Li, X., Guo, Z., Dai, X., Lin, Y., Jin, J., Zhu, F., et al., 2020a. Deep imitation learning for traffic signal control and operations based on graph convolutional neural networks. In: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). September 20-23, 2020, Rhodes, Greece. IEEE, pp. 1–6.
Lu, S., Liu, X., Dai, S., 2008. Q-learning for adaptive traffic signal control based on delay minimization strategy. In: 2008 IEEE International Conference on Networking, Sensing and Control. April 6-8, 2008, Sanya, China. IEEE, pp. 687–691.
Lubars, J., Gupta, H., Chinchali, S., Li, L., Raja, A., Srikant, R., et al., 2021. Combining reinforcement learning with model predictive control for on-ramp merging. In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC). September 19-22, 2021, Indianapolis. IEEE, USA, pp. 942–947.
Nishi, T., Otaki, K., Hayakawa, K., Yoshimura, T., 2018. Traffic signal control based on reinforcement learning with graph convolutional neural nets. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC). November 4-7, 2018. IEEE, Maui, HI, USA, pp. 877–883.
Nishitani, I., Yang, H., Guo, R., Keshavamurthy, S., Oguchi, K., 2020. Deep merging: vehicle merging controller based on deep reinforcement learning with embedding network. In: 2020 IEEE International Conference on Robotics and Automation (ICRA). May 31 - August 31, 2020, Paris, France. IEEE, pp. 216–221.
Pinto, L., Davidson, J., Sukthankar, R., Gupta, A., 2017. Robust adversarial reinforcement learning. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70. ACM, Sydney, NSW, Australia. New York, pp. 2817–2826. August 6-11, 2017.
Rizzo, S.G., Vantini, G., Chawla, S., 2019. Time critic policy gradient methods for traffic signal control in complex and congested scenarios. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. August 4-8, 2019. ACM, Anchorage, AK, USA. New York, pp. 1654–1664.
Rodrigues, F., Azevedo, C.L., 2019. Towards robust deep reinforcement learning for traffic signal control: demand surges, incidents and sensor failures. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC). October 27-30, 2019, Auckland, New Zealand. IEEE, pp. 3559–3566.
Shabestary, S.M.A., Abdulhai, B., 2018. Deep learning vs. discrete reinforcement learning for adaptive traffic signal control. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC). November 4-7, 2018. IEEE, Maui, HI, USA, pp. 286–293.
Thorpe, T.L., Anderson, C., 1996. Traac Light Control Using SARSA with Three State Representations. Technical report, Citeseer.
Van der Pol, E., Oliehoek, F., 2016. Coordinated deep reinforcement learners for traffic light control. In: Proceedings of Learning, Inference and Control of Multi-Agent Systems (At NIPS 2016), vol. 8, pp. 21–38.
Wang, P., Chan, C.Y., 2017. Formulation of deep reinforcement learning architecture toward autonomous driving for on-ramp merge. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). October 16-19, 2017, Yokohama, Japan. IEEE, pp. 1–6.
Wang, M., Wu, L., Li, J., Wu, D., Ma, C., 2022a. Urban traffic signal control with reinforcement learning from demonstration data. In: 2022 International Joint Conference on Neural Networks (IJCNN). July 18-23, 2022, Padua, Italy. IEEE, pp. 1–8.
Wei, H., Zheng, G., Yao, H., Li, Z., 2018. IntelliLight: a reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. August 19-23, 2018. ACM, London, United Kingdom. New York, pp. 2496–2505.
Wei, H., Chen, C., Zheng, G., Wu, K., Gayah, V., Xu, K., et al., 2019. PressLight: learning max pressure control to coordinate traffic signals in arterial network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. August 4-8, 2019. ACM, Anchorage, AK, USA. New York, pp. 1290–1298.
Wiering, M., 2000. Multi-agent reinforcement leraning for traffic light control. In: Proceedings of the Seventeenth International Conference on Machine Learning. ACM, New York, pp. 1151–1158.
Xiong, Y., Zheng, G., Xu, K., Li, Z., 2019. Learning traffic signal control from demonstrations. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. November 3-7, 2019. ACM, Beijing, China. New York, pp. 2289–2292.
Zhang, L., Wu, Q., Shen, J., Lü, L., Du, B., Wu, J., 2022. Expression might be enough: Representing pressure and demand for reinforcement learning based traffic signal control. In: International Conference on Machine Learning. PMLR, pp. 26645–26654.
Zhang, H., Liu, C., Zhang, W., Zheng, G., Yu, Y., 2020. GeneraLight: improving environment generalization of traffic signal control via meta reinforcement learning. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management. October 19-23, 2020, Virtual Event, Ireland. ACM, New York, pp. 1783–1792.
Zheng, G., Xiong, Y., Zang, X., Feng, J., Wei, H., Zhang, H., et al., 2019a. Learning phase competition for traffic signal control. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. November 3-7, 2019. ACM, Beijing, China. New York, pp. 1963–1972.