Machine learning is often cited as a new paradigm in control theory, but is also often viewed as empirical hardware and less intuitive for students than classical model-based methods.This is particularly the case for reinforcement learning, an approach that does not require any mathematical model to drive a system inside an unknown environment.This lack of intuition can be an obstacle to design experiments and implement this approach.
Reversely there is a need to gain experience and intuition from experiments.In this article, we propose a general framework to reproduce successful experiments and simulations based on the inverted pendulum, a classic problem often used as a benchmark to evaluate control strategies.Two algorithms (basic Q-Learning and Deep Q-Networks (DQN)) are introduced, both in experiments and in simulation with a virtual environment, to give a comprehensive understanding of the approach and discuss its implementation on real systems.
In experiments, we show Thermometers - Thermometer Cover Sheaths that learning over a few hours is enough to control the pendulum with high accuracy.Simulations provide insights about the effect of each physical parameter and tests the feasibility and robustness of the approach.