Tianshu Chu
Cited by
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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
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
Multi-agent reinforcement learning for networked system control
T Chu, S Chinchali, S Katti
arXiv preprint arXiv:2004.01339, 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
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
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
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
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
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
Large-scale traffic grid signal control with regional reinforcement learning
T Chu, S Qu, J Wang
2016 american control conference (acc), 815-820, 2016
Cloud resource allocation for cloud-based automotive applications
Z Li, T Chu, IV Kolmanovsky, X Yin, X Yin
Mechatronics 50, 356-365, 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
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
Traffic signal control by distributed reinforcement learning with min-sum communication
T Chu, J Wang
2017 american control conference (acc), 5095-5100, 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
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
Traffic signal control with macroscopic fundamental diagrams
T Chu, J Wang
2015 American Control Conference (ACC), 4380-4385, 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
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
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
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