  We introduce a control-tutored reinforcement learning (CTRL) algorithm. The
idea is to enhance tabular learning algorithms by means of a control strategy
with limited knowledge of the system model. By tutoring the learning process,
the learning rate can be substantially reduced. We use the classical problem of
stabilizing an inverted pendulum as a benchmark to numerically illustrate the
advantages and disadvantages of the approach.
