Detail publikace

Continuous Q-learning application

VĚCHET, S., KREJSA, J.

Originální název

Continuous Q-learning application

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

Standard algorithm of Q-Learning is limited by discrete states and actions and Q-function is usually represented as discrete table. To avoid this obstacle and extend the use of Q-learning for continuous states and actions the algorithm must be modified and such modification is presented in the paper. Straightforward way is to replace discrete table with suitable approximator. A number of approximators can be used, with respect to memory and computational requirements the local approximator is particularly favorable. We have used Locally Weighted Regression (LWR) algorithm. The paper discusses advantages and disadvantages of modified algorithm demonstrated on simple control task.

Klíčová slova

Q-learning, Machine learning, Locall approximators

Autoři

VĚCHET, S., KREJSA, J.

Rok RIV

2004

Vydáno

10. 5. 2004

Nakladatel

Institute of Thermonechanics Academy of Sciences of the Czech Republic, Prague 2004

Místo

Prague

ISBN

80-85918-88-9

Kniha

Engineering Mechanics 2004

Číslo edice

1

Strany od

307

Strany do

308

Strany počet

2

BibTex

@inproceedings{BUT14018,
  author="Stanislav {Věchet} and Jiří {Krejsa}",
  title="Continuous Q-learning application",
  booktitle="Engineering Mechanics 2004",
  year="2004",
  number="1",
  pages="2",
  publisher="Institute of Thermonechanics Academy of Sciences of the Czech Republic, Prague 2004",
  address="Prague",
  isbn="80-85918-88-9"
}