Reference
W. Remmerswaal, D. Sun, A. Jamshidnejad, and B. De Schutter, "Combined MPC and
reinforcement learning for traffic signal control in urban traffic networks,"
Proceedings of the 2022 26th International Conference on
System Theory, Control and Computing (ICSTCC), Sinaia, Romania, pp.
432-439, Oct. 2022.
Abstract
In general, the performance of model-based controllers cannot be guaranteed
under model uncertainties or disturbances, while learning-based controllers
require an extensively sufficient training process to perform well. These
issues especially hold for large-scale nonlinear systems such as urban traffic
networks. In this paper, a new framework is proposed by combining model
predictive control (MPC) and reinforcement learning (RL) to provide desired
performance for urban traffic networks even during the learning process,
despite model uncertainties and disturbances. MPC and RL complement each other
very well, since MPC provides a sub-optimal and constraint-satisfying control
input while RL provides adaptive control laws and can handle uncertainties and
disturbances. The resulting combined framework is applied for traffic signal
control (TSC) of an urban traffic network. A case study is carried out to
compare the performance of the proposed framework and other baseline
controllers. Results show that the proposed combined framework outperforms
conventional control methods under system uncertainties, in terms of reducing
traffic congestion.
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BibTeX
@inproceedings{RemSun:22-010,
author = {Remmerswaal, Willemijn and Sun, Dingshan and Jamshidnejad,
Anahita and De Schutter, Bart},
title = {Combined {MPC} and Reinforcement Learning for Traffic Signal
Control in Urban Traffic Networks},
booktitle = {Proceedings of the 2022 26th International Conference on System
Theory, Control and Computing (ICSTCC)},
address = {Sinaia, Romania},
pages = {432--439},
month = oct,
year = {2022}
}