Combining MPC and reinforcement learning in a model-reference framework for urban traffic signal control

Both model predictive control (MPC) and reinforcement learning (RL) have shown promising results in the control of traffic signals in urban traffic networks. There are, however, a few drawbacks. MPC controllers are not adaptive and therefore perform suboptimal in the presence of the uncertainties that always occur in urban traffic systems....

Parametrized Model Predictive Control in Urban Traffic Networks: Towards real-time implementation

Model Predictive Control (MPC) has shown promising results in the control of urban traffic networks, but has one major drawback. The, often nonlinear, optimization that has to be performed at every control time step is computationally too complex to use MPC controllers for real-time implementations (i.e. when the online optimization is performed...