  While reinforcement learning has made great improvements, state-of-the-art
algorithms can still struggle with seemingly simple set-point feedback control
problems. One reason for this is that the learned controller may not be able to
excite the system dynamics well enough initially, and therefore it can take a
long time to get data that is informative enough to learn for good control. The
paper contributes by augmentation of reinforcement learning with a simple
guiding feedback controller, for example, a proportional controller. The key
advantage in set point control is a much improved excitation that improves the
convergence properties of the reinforcement learning controller significantly.
This can be very important in real-world control where quick and accurate
convergence is needed. The proposed method is evaluated with simulation and on
a real-world double tank process with promising results.
