Publication detail
Q-Learning: From Discrete to Continuous Representation
VĚCHET, S. KREJSA, J.
Czech title
Modifikace metody Q-učení z diskrétní na spojitou
English title
Q-Learning: From Discrete to Continuous Representation
Type
journal article - other
Language
en
Original abstract
Q-learning standard algorithm is restricted by using discrete states and actions. In this case Q-function is usually represented as a discrete table of Q-values. Conversion of continuous variables to adequate discrete variables evokes some problems. Problems can be avoided if the continuous algorithm of Q-learning is used. In this paper we discus method, which is used to convert discrete to continuous algorithm. The method used suitable approximator to replace the discrete table. We choose local approximator called Locally Weighted Regression (LWR) (Atketson &Moore & Shaal, 1996) from the group of memory based approximators.
Czech abstract
Tento článek pojednává o způsobu převedení standardní metody Q-učení, která je pouze diskrétní, na spojitou. K tomuto účelu je použit jednoduchý lokální aproximátor Lokálně vážená regrese (LVR). Tento aproximátor slouží k převedení diskrétní tabulky Q-hodnot na spojitou Q-funkci.
English abstract
Q-learning standard algorithm is restricted by using discrete states and actions. In this case Q-function is usually represented as a discrete table of Q-values. Conversion of continuous variables to adequate discrete variables evokes some problems. Problems can be avoided if the continuous algorithm of Q-learning is used. In this paper we discus method, which is used to convert discrete to continuous algorithm. The method used suitable approximator to replace the discrete table. We choose local approximator called Locally Weighted Regression (LWR) (Atketson &Moore & Shaal, 1996) from the group of memory based approximators.
Keywords in English
Q-learning, Machine learning, Locally Weighted Regression
RIV year
2004
Released
23.08.2004
Location
Warsaw, Poland
ISSN
0033-2089
Journal
Elektronika
Volume
XVL
Number
8
Pages count
3
BIBTEX
@article{BUT42197,
author="Stanislav {Věchet} and Jiří {Krejsa},
title="Q-Learning: From Discrete to Continuous Representation",
journal="Elektronika",
year="2004",
volume="XVL",
number="8",
month="August",
address="Warsaw, Poland",
issn="0033-2089"
}