Publication result detail

Q-Learning: From Discrete to Continuous Representation

VĚCHET, S., KREJSA, J.

Original Title

Q-Learning: From Discrete to Continuous Representation

English Title

Q-Learning: From Discrete to Continuous Representation

Type

Peer-reviewed article not indexed in WoS or Scopus

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.

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

Q-learning, Machine learning, Locally Weighted Regression

Key words in English

Q-learning, Machine learning, Locally Weighted Regression

Authors

VĚCHET, S., KREJSA, J.

Released

23.08.2004

Location

Warsaw, Poland

ISBN

0033-2089

Periodical

Elektronika

Volume

XVL

Number

8

State

Republic of Poland

Pages from

12

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"
}