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  • Standard algorithm of Q-Learning is limited by discrete states and actions and Q-function is usually represented as discrete table. To avoid this obstacle and extend the use of Q-learning for continuous states and actions the algorithm must be modified and such modification is presented in the paper. Straightforward way is to replace discrete table with suitable approximator. A number of approximators can be used, with respect to memory and computational requirements the local approximator is particularrly favorable. We have used Locally Weighted Regression (LWR) algorithm. The paper discusses advantages and disadvantages of modified algorithm demonstrated on simple control task.
  • Standard algorithm of Q-Learning is limited by discrete states and actions and Q-function is usually represented as discrete table. To avoid this obstacle and extend the use of Q-learning for continuous states and actions the algorithm must be modified and such modification is presented in the paper. Straightforward way is to replace discrete table with suitable approximator. A number of approximators can be used, with respect to memory and computational requirements the local approximator is particularrly favorable. We have used Locally Weighted Regression (LWR) algorithm. The paper discusses advantages and disadvantages of modified algorithm demonstrated on simple control task. (en)
  • Standardní algoritmus metody Q-učení je omezen používáním diskrétních stavů a akcí. V tomto případě je Q-funkce representována jako diskrétní tabulka. Metoda popisovaná v tomto příspěvku se snaží obejít problém s diskretizací tím, že je od počátku navržena jako spojitá. Diskrétní tabulka Q-hodnot je nahrazena vhodným aproximátorem. V tomto příspěvku hodnotíme výhody a nevýhody spojitého Q-učení oproti jeho diskrétní variantě. (cs)
Title
  • Continuous Q-learning application
  • Continuous Q-learning application (en)
  • Praktická aplikace Q-učení (cs)
skos:prefLabel
  • Continuous Q-learning application
  • Continuous Q-learning application (en)
  • Praktická aplikace Q-učení (cs)
skos:notation
  • RIV/00216305:26210/04:PU43468!RIV/2005/MSM/262105/N
http://linked.open.../vavai/riv/strany
  • 307-308
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(AV0Z2076919), Z(MSM 262100024)
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
  • 558709
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26210/04:PU43468
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Q-learning, Machine learning, Locall approximators (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [4FE447A9CDD7]
http://linked.open...v/mistoKonaniAkce
  • Svratka
http://linked.open...i/riv/mistoVydani
  • Prague
http://linked.open...i/riv/nazevZdroje
  • Engineering Mechanics 2004
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Krejsa, Jiří
  • Věchet, Stanislav
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • Ústav termomechaniky AV ČR
https://schema.org/isbn
  • 80-85918-88-9
http://localhost/t...ganizacniJednotka
  • 26210
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