Reinforcement learning approach to control an inverted pendulum: A general framework for educational purposes.

Reinforcement learning approach to control an inverted pendulum: A general framework for educational purposes.

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Machine learning is often cited as a new paradigm in control theory, but is also often viewed as empirical 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 kicker pro comp 10 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 majicontrast red discuss its implementation on real systems.

In experiments, we show 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.