Q-learning-based model-free swing up control of an inverted pendulum

Alessio Ghio, Oscar E. Ramos

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

4 Citas (Scopus)

Resumen

An inverted pendulum is a high non-linear, chaotic and dynamically complex system, which presents problems for traditional controllers that require feedback loops and a precise dynamic model of the system. Reinforcement learning is an promising approach, since it does not need the dynamic model and generates autonomous actions based on experience. However, solving a control problem with reinforcement learning is challenging, because every dynamic system has a continuous state space. In this paper, an algorithm that uses Q-learning with function approximation is proposed to control an inverted pendulum. The algorithm consists of two stages, one for swing up, and another for the control at upright position. Results show that the proposed approach reaches the control objectives.

Idioma originalInglés
Título de la publicación alojadaProceedings of the 2019 IEEE 26th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2019
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728136462
DOI
EstadoPublicada - ago. 2019
Evento26th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2019 - Lima, Perú
Duración: 12 ago. 201914 ago. 2019

Serie de la publicación

NombreProceedings of the 2019 IEEE 26th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2019

Conferencia

Conferencia26th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2019
País/TerritorioPerú
CiudadLima
Período12/08/1914/08/19

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