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

ABSTRACT: 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

SUBMITTER: Israilov S 

PROVIDER: S-EPMC9925229 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature