  We present an architecture where a feedback controller derived on an
approximate model of the environment assists the learning process to enhance
its data efficiency. This architecture, which we term as Control-Tutored
Q-learning (CTQL), is presented in two alternative flavours. The former is
based on defining the reward function so that a Boolean condition can be used
to determine when the control tutor policy is adopted, while the latter, termed
as probabilistic CTQL (pCTQL), is instead based on executing calls to the tutor
with a certain probability during learning. Both approaches are validated, and
thoroughly benchmarked against Q-Learning, by considering the stabilization of
an inverted pendulum as defined in OpenAI Gym as a representative problem.
