Tutoring Reinforcement Learning via Feedback Control

We introduce a reinforcement learning algorithm assisted by a feedback controller. The idea is to enhance tabular learning algorithms by means of a control strategy with limited knowledge of the system model. We show that, by tutoring the learning process, the algorithm converges more quickly than the tabular Q-learning strategy. We use the classical problem of stabilizing an inverted pendulum as a benchmark to numerically illustrate the advantages and disadvantages of the approach.