Detail publikace

Using Modified Q-learning With LWR for Inverted Pendulum Control

VĚCHET, S. KREJSA, J. BŘEZINA, T.

Anglický název

Using Modified Q-learning With LWR for Inverted Pendulum Control

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

2003-03-24

Nakladatel

Institute of Mechanics of Solids, Brno University of Technology

Místo

Brno

ISBN

80-214-2312-9

Kniha

Mechatronics, Robotics and Biomachanics 2003

Strany od–do

91–

Počet stran

2

BIBTEX


@inproceedings{BUT9715,
  author="Stanislav {Věchet} and Jiří {Krejsa} and Tomáš {Březina}",
  title="Using Modified Q-learning With LWR for Inverted Pendulum Control",
  booktitle="Mechatronics, Robotics and Biomachanics 2003",
  year="2003",
  pages="2",
  publisher="Institute of Mechanics of Solids, Brno University of Technology",
  address="Brno",
  isbn="80-214-2312-9"
}