Publication detail
Q-LEARNING USED FOR CONTROL OF AMB: REDUCED STATE DEFINITION
BŘEZINA, T. KREJSA, J.
English title
Q-LEARNING USED FOR CONTROL OF AMB: REDUCED STATE DEFINITION
Type
Paper in proceedings (conference paper)
Language
en
Original abstract
A great intention is lately focused on Reinforcement Learning (RL) methods. Previous work showed that stochastic strategy improved model free RL method known as Q-learning used on active magnetic bearing (AMB) model. So far the position, velocity and acceleration were used to describe the state of the system. This paper shows simplified version of controller which uses reduced state definition – position and velocity only. Furthermore the controlled initial conditions area and its development during learning are shown. Numerical experiments proved that simplified controller version is fully capable of AMB control.
Keywords in English
Reinforcement Learning, Q-learning, Active Magnetic Bearing
Released
2002-06-05
Publisher
Brno University of Technology, Faculty of Mechanical Engineering
Location
Brno
ISBN
80-214-2135-5
Book
Mendel 2002
Pages from–to
347–
Pages count
6
BIBTEX
@inproceedings{BUT10054,
author="Tomáš {Březina} and Jiří {Krejsa}",
title="Q-LEARNING USED FOR CONTROL OF AMB: REDUCED STATE DEFINITION",
booktitle="Mendel 2002",
year="2002",
number="1",
pages="6",
publisher="Brno University of Technology, Faculty of Mechanical Engineering",
address="Brno",
isbn="80-214-2135-5"
}