Detail publikace

Q-LEARNING USED FOR CONTROL OF AMB: REDUCED STATE DEFINITION

BŘEZINA, T., KREJSA, J.

Originální název

Q-LEARNING USED FOR CONTROL OF AMB: REDUCED STATE DEFINITION

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

Reinforcement Learning, Q-learning, Active Magnetic Bearing

Autoři

BŘEZINA, T., KREJSA, J.

Rok RIV

2002

Vydáno

5. 6. 2002

Nakladatel

Brno University of Technology, Faculty of Mechanical Engineering

Místo

Brno

ISBN

80-214-2135-5

Kniha

Mendel 2002

Číslo edice

1

Strany od

347

Strany do

352

Strany počet

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"
}