Publication detail

Q-Learning: From Discrete to Continuous Representation

VĚCHET, S., KREJSA, J.

Original Title

Q-Learning: From Discrete to Continuous Representation

Type

journal article - other

Language

English

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.

Keywords

Q-learning, Machine learning, Locally Weighted Regression

Authors

VĚCHET, S., KREJSA, J.

RIV year

2004

Released

23. 8. 2004

Location

Warsaw, Poland

ISBN

0033-2089

Periodical

Elektronika

Year of study

XVL

Number

8

State

Republic of Poland

Pages from

12

Pages to

14

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",
  pages="3",
  issn="0033-2089"
}