FDMS with Q-Learning: A Neuro-Fuzzy Approach to Partially Observable Markov Decision Problems

FDMS with Q-Learning: A Neuro-Fuzzy Approach to Partially Observable Markov Decision Problems

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Finding optimal solutions to Partially Observable Markov Decision Problems is known to be NP-hard. This paper describes a novel neuro-fuzzy approach to obtain fast, robust and easily interpreted solutions by utilizing a combination of several learning techniques including neural networks, fuzzy deci...

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Bibliographic Details
Main Authors: Toygar Karadeniz, Levent Akin
Format: Article
Language:English
Published: SAGE Publishing 2004-12-01
Series:International Journal of Advanced Robotic Systems
Online Access:https://doi.org/10.5772/5817
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spelling doaj-7b743a4080354a5a9de2e92045f1a7a12020-11-25T03:43:30ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142004-12-01110.5772/581710.5772_5817FDMS with Q-Learning: A Neuro-Fuzzy Approach to Partially Observable Markov Decision ProblemsToygar KaradenizLevent AkinFinding optimal solutions to Partially Observable Markov Decision Problems is known to be NP-hard. This paper describes a novel neuro-fuzzy approach to obtain fast, robust and easily interpreted solutions by utilizing a combination of several learning techniques including neural networks, fuzzy decision making and Q-learning.https://doi.org/10.5772/5817
collection DOAJ
language English
format Article
sources DOAJ
author Toygar Karadeniz
Levent Akin
spellingShingle Toygar Karadeniz
Levent Akin
FDMS with Q-Learning: A Neuro-Fuzzy Approach to Partially Observable Markov Decision Problems
International Journal of Advanced Robotic Systems
author_facet Toygar Karadeniz
Levent Akin
author_sort Toygar Karadeniz
title FDMS with Q-Learning: A Neuro-Fuzzy Approach to Partially Observable Markov Decision Problems
title_short FDMS with Q-Learning: A Neuro-Fuzzy Approach to Partially Observable Markov Decision Problems
title_full FDMS with Q-Learning: A Neuro-Fuzzy Approach to Partially Observable Markov Decision Problems
title_fullStr FDMS with Q-Learning: A Neuro-Fuzzy Approach to Partially Observable Markov Decision Problems
title_full_unstemmed FDMS with Q-Learning: A Neuro-Fuzzy Approach to Partially Observable Markov Decision Problems
title_sort fdms with q-learning: a neuro-fuzzy approach to partially observable markov decision problems
publisher SAGE Publishing
series International Journal of Advanced Robotic Systems
issn 1729-8814
publishDate 2004-12-01
description Finding optimal solutions to Partially Observable Markov Decision Problems is known to be NP-hard. This paper describes a novel neuro-fuzzy approach to obtain fast, robust and easily interpreted solutions by utilizing a combination of several learning techniques including neural networks, fuzzy decision making and Q-learning.
url https://doi.org/10.5772/5817
work_keys_str_mv AT toygarkaradeniz fdmswithqlearninganeurofuzzyapproachtopartiallyobservablemarkovdecisionproblems
AT leventakin fdmswithqlearninganeurofuzzyapproachtopartiallyobservablemarkovdecisionproblems
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