Detail publikace

Using Q-Learning with LWR in continuous space

VĚCHET, S. MIČEK, P. KREJSA, J.

Anglický název

Using Q-Learning with LWR in continuous space

Typ

Stať ve sborníku v databázi WoS či Scopus

Jazyk

en

Originální abstrakt

Locally Weighted Learning (LWR) is a class of approximations, based on a local model. In this paper we demonstrate using LWR together with Q-learning for control tasks. Q-learning is the most effective and popular algorithm which belongs to the Reinforcement Learning algorithms group. This algorithm works with rewards and penalties. The most common representation of Q-function is the table. The table must be replaced by suitable approximator if use of continuous states is required. LWR is one of possible approximators. To get the first impression on application of LWR together with modified Q-learning for the control task a simple model of inverted pendulum was created and proposed method was applied on this model.

Klíčová slova anglicky

Q-Learning, LWR, Continuous Space

Vydáno

2003-06-18

Nakladatel

Alexander Dubček University of Trenčí, Faculty of Mechatronics

Místo

Trenčín

ISBN

80-88914-92-2

Kniha

Proceedings of 6th international symposium on Mechatronics

Strany od–do

58–

Počet stran

4

BIBTEX


@inproceedings{BUT8367,
  author="Stanislav {Věchet} and Pavel {Miček} and Jiří {Krejsa}",
  title="Using Q-Learning with LWR in continuous space",
  booktitle="Proceedings of 6th international symposium on Mechatronics",
  year="2003",
  pages="4",
  publisher="Alexander Dubček University of Trenčí, Faculty of Mechatronics",
  address="Trenčín",
  isbn="80-88914-92-2"
}