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Use of continous action reinforcement learning automata for asynchronous electromotro control

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F04%3APU45626" target="_blank" >RIV/00216305:26210/04:PU45626 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    čeština

  • Original language name

    Use of continous action reinforcement learning automata for asynchronous electromotro control

  • Original language description

    Relatively unknown reinforcement learning algorithm, so called continuous action reinforcement learning automaton, is presented in this contribution. Automaton learning algorithm is based on rewarding, that gradually evolves set of probability densities.This set is consequently used for action set determination. Simulation study describing learning and behavior of asynchronous electromotor control is further presented. Standard PSD controller is used whose parameter values represent actions of three independent automata. The aim of online learning process is to minimize mean square of control error. Here described learning algorithm is simple to implement, robust to high level of noise.

  • Czech name

    Use of continous action reinforcement learning automata for asynchronous electromotro control

  • Czech description

    Relatively unknown reinforcement learning algorithm, so called continuous action reinforcement learning automaton, is presented in this contribution. Automaton learning algorithm is based on rewarding, that gradually evolves set of probability densities.This set is consequently used for action set determination. Simulation study describing learning and behavior of asynchronous electromotor control is further presented. Standard PSD controller is used whose parameter values represent actions of three independent automata. The aim of online learning process is to minimize mean square of control error. Here described learning algorithm is simple to implement, robust to high level of noise.

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    JD - Use of computers, robotics and its application

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Others

  • Publication year

    2004

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Enigneering Mechanics 2004, National Conference with International Participation

  • ISBN

    80-85918-88-9

  • ISSN

  • e-ISSN

  • Number of pages

    2

  • Pages from-to

  • Publisher name

    Institute of Thermomechanics, Academy of Sciences of the Czec Republic

  • Place of publication

    Svratka

  • Event location

    Svratka

  • Event date

    May 10, 2004

  • Type of event by nationality

    CST - Celostátní akce

  • UT code for WoS article