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VĚCHET, S., KREJSA, J., BŘEZINA, T.
Originální název
Using Modified Q-learning With LWR for Inverted Pendulum Control
Anglický název
Druh
Stať ve sborníku v databázi WoS či Scopus
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.
Anglický abstrakt
Klíčová slova
Q-Learning, LWR, Continuous Space
Klíčová slova v angličtině
Autoři
Vydáno
24.03.2003
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
91
Strany počet
2
Plný text v Digitální knihovně
http://hdl.handle.net/
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" }