Detail publikace

Reinforcement learning model: control of nonlinear and unstable processes

BŘEZINA, T., EHRENBERGER, Z., KRATOCHVÍL, C.

Originální název

Reinforcement learning model: control of nonlinear and unstable processes

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

Some experiences with using of the reinforcement learning model at control of nonlinear unstable processes are published in this paper. Control process is characterized by extensive depth in such cases, so the learning is computationally very demanding. We propose both using of nonlinear grid of the Q-function approximation table and also using of the learning conception “by expert observation“. Learning off (optimal) control policy is not based on blind searching a state space, but it is in progress with the help of further component, that is able to control the process. Problems are studied on active magnetic bearing one-mass model.

Klíčová slova

control algorithm, Q-learning, neural network

Autoři

BŘEZINA, T., EHRENBERGER, Z., KRATOCHVÍL, C.

Rok RIV

2002

Vydáno

14. 6. 2001

Nakladatel

Institute of Termomechanics Academy of Sience of the Czech Republic, Prague 2001

Místo

Svratka

ISBN

80-85918-64-1

Kniha

Inženýrská Mechanika 2001

Číslo edice

1

Strany od

40

Strany do

41

Strany počet

2

BibTex

@{BUT69519
}