Publication detail
Continuous Q-learning application
VĚCHET, S. KREJSA, J.
English title
Continuous Q-learning application
Type
Paper in proceedings (conference paper)
Language
en
Original abstract
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.
Keywords in English
Q-learning, Machine learning, Locall approximators
Released
2004-05-10
Publisher
Institute of Thermonechanics Academy of Sciences of the Czech Republic, Prague 2004
Location
Prague
ISBN
80-85918-88-9
Book
Engineering Mechanics 2004
Pages from–to
307–
Pages count
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"
}