Publication detail

Q-Learning: From Discrete to Continuous Representation

VĚCHET, S. KREJSA, J.

English title

Q-Learning: From Discrete to Continuous Representation

Type

Peer-reviewed article not indexed in WoS or Scopus

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.

Keywords in English

Q-learning, Machine learning, Locally Weighted Regression

Released

2004-08-23

Location

Warsaw, Poland

ISSN

0033-2089

Journal

Elektronika

Volume

XVL

Number

8

Pages from–to

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"
}