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.
Tutoring Reinforcement Learning via Feedback Control
Russo Giovanni;
2021-01-01
Abstract
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.File in questo prodotto:
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