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.
Bibtex entry:
@inproceedings{RemSun:22-010,