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  • 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 wiith the help of further component, that is able to control the process. Problems are studied on active magnetic bearing one-mass model.
  • 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 wiith the help of further component, that is able to control the process. Problems are studied on active magnetic bearing one-mass model. (en)
Title
  • Reinforcement learning model: control of nonlinear and unstable processes
  • Reinforcement learning model: control of nonlinear and unstable processes (en)
skos:prefLabel
  • Reinforcement learning model: control of nonlinear and unstable processes
  • Reinforcement learning model: control of nonlinear and unstable processes (en)
skos:notation
  • RIV/00216305:26210/01:PU23020!RIV/2003/GA0/262103/N
http://linked.open.../vavai/riv/strany
  • 40-41
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA101/00/1471)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 694358
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26210/01:PU23020
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • control algorithm, Q-learning, neural network (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [45174964FFAA]
http://linked.open...v/mistoKonaniAkce
  • Svratka
http://linked.open...i/riv/mistoVydani
  • Svratka
http://linked.open...i/riv/nazevZdroje
  • Inženýrská Mechanika 2001
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...ocetUcastnikuAkce
http://linked.open...nichUcastnikuAkce
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Březina, Tomáš
  • Kratochvíl, Ctirad
  • Ehrenberger, Zdeněk
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Ústav termomechaniky AV ČR
https://schema.org/isbn
  • 80-85918-64-1
http://localhost/t...ganizacniJednotka
  • 26210
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