| Multi-agent deep reinforcement learning for large-scale traffic signal control T Chu, J Wang, L Codecą, Z Li IEEE Transactions on Intelligent Transportation Systems 21 (3), 1086-1095, 2019 | 730 | 2019 |
| Cellular network traffic scheduling with deep reinforcement learning S Chinchali, P Hu, T Chu, M Sharma, M Bansal, R Misra, M Pavone, ... Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 243 | 2018 |
| Multi-agent reinforcement learning for networked system control T Chu, S Chinchali, S Katti arXiv preprint arXiv:2004.01339, 2020 | 107 | 2020 |
| Model-based deep reinforcement learning for CACC in mixed-autonomy vehicle platoon T Chu, U Kalabić 2019 IEEE 58th Conference on Decision and Control (CDC), 4079-4084, 2019 | 60 | 2019 |
| Powernet: Multi-agent deep reinforcement learning for scalable powergrid control D Chen, K Chen, Z Li, T Chu, R Yao, F Qiu, K Lin IEEE Transactions on Power Systems 37 (2), 1007-1017, 2021 | 59 | 2021 |
| Safe reinforcement learning: Learning with supervision using a constraint-admissible set Z Li, U Kalabić, T Chu 2018 Annual American Control Conference (ACC), 6390-6395, 2018 | 55 | 2018 |
| Neural networks meet physical networks: Distributed inference between edge devices and the cloud SP Chinchali, E Cidon, E Pergament, T Chu, S Katti Proceedings of the 17th ACM Workshop on Hot Topics in Networks, 50-56, 2018 | 52 | 2018 |
| Knowledge source strategy and enterprise innovation performance: dynamic analysis based on machine learning X Jin, J Wang, T Chu, J Xia Technology Analysis & Strategic Management 30 (1), 71-83, 2018 | 42 | 2018 |
| A centralized reinforcement learning approach for proactive scheduling in manufacturing S Qu, T Chu, J Wang, J Leckie, W Jian 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation …, 2015 | 41 | 2015 |
| Large-scale traffic grid signal control with regional reinforcement learning T Chu, S Qu, J Wang 2016 american control conference (acc), 815-820, 2016 | 39 | 2016 |
| Cloud resource allocation for cloud-based automotive applications Z Li, T Chu, IV Kolmanovsky, X Yin, X Yin Mechatronics 50, 356-365, 2018 | 25 | 2018 |
| Training drift counteraction optimal control policies using reinforcement learning: An adaptive cruise control example Z Li, T Chu, IV Kolmanovsky, X Yin IEEE transactions on intelligent transportation systems 19 (9), 2903-2912, 2017 | 22 | 2017 |
| Comuptional reasoning and learning for smart manufacturing under realistic conditions S Qu, R Jian, T Chu, J Wang, T Tan 2014 International Conference on Behavioral, Economic, and Socio-Cultural …, 2014 | 22 | 2014 |
| Traffic signal control by distributed reinforcement learning with min-sum communication T Chu, J Wang 2017 american control conference (acc), 5095-5100, 2017 | 21 | 2017 |
| Dynamics-enabled safe deep reinforcement learning: Case study on active suspension control Z Li, T Chu, U Kalabić 2019 IEEE conference on control technology and applications (CCTA), 585-591, 2019 | 18 | 2019 |
| Large-scale multi-agent reinforcement learning using image-based state representation T Chu, S Qu, J Wang 2016 IEEE 55th Conference on Decision and Control (CDC), 7592-7597, 2016 | 15 | 2016 |
| Traffic signal control with macroscopic fundamental diagrams T Chu, J Wang 2015 American Control Conference (ACC), 4380-4385, 2015 | 14 | 2015 |
| Kernel-based reinforcement learning for traffic signal control with adaptive feature selection T Chu, J Wang, J Cao 53rd IEEE Conference on Decision and Control, 1277-1282, 2014 | 13 | 2014 |
| Modeling and optimizing the performance of PVC/PVB ultrafiltration membranes using supervised learning approaches L Chi, J Wang, T Chu, Y Qian, Z Yu, D Wu, Z Zhang, Z Jiang, JO Leckie RSC advances 6 (33), 28038-28046, 2016 | 12 | 2016 |
| Multi-agent bootstrapped deep q-network for large-scale traffic signal control T Tan, T Chu, J Wang 2020 IEEE Conference on Control Technology and Applications (CCTA), 358-365, 2020 | 11 | 2020 |
