Using Q-Learning with LWR in continuous space

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F03%3APU38794" target="_blank" >RIV/00216305:26210/03:PU38794 - isvavai.cz</a>

Alternative codes found

RIV/61388998:_____/03:51030193

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Using Q-Learning with LWR in continuous space

Original language description

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 possiblle 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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

BC - Theory and management systems

OECD FORD branch

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Project

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Continuities

Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2003

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of 6th international symposium on Mechatronics

ISBN

80-88914-92-2

ISSN

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e-ISSN

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Number of pages

4

Pages from-to

58-61

Publisher name

Alexander Dubček University of Trenčí, Faculty of Mechatronics

Place of publication

Trenčín

Event location

Trenčanské Teplice, SLOVAKIA

Event date

Jun 18, 2003

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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