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Article Dans Une Revue PLoS ONE Année : 2023

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

Résumé

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 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 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.
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Dates et versions

hal-03999495 , version 1 (21-02-2023)

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Sardor Israilov, Li Fu, Jesús Sánchez-Rodríguez, Franco Fusco, Guillaume Allibert, et al.. Reinforcement learning approach to control an inverted pendulum: A general framework for educational purposes. PLoS ONE, 2023, 18 (2), pp.e0280071. ⟨10.1371/journal.pone.0280071⟩. ⟨hal-03999495⟩
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