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

Toygar Karadeniz | Zendy; H. Levent Akın | Zendy

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

Author(s) -

Toygar Karadeniz,

H. Levent Akın

Publication year - 2004

Publication title -

international journal of advanced robotic systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.394

H-Index - 46

eISSN - 1729-8814

pISSN - 1729-8806

DOI - 10.5772/5817

Subject(s) - computer science , observable , markov decision process , fuzzy logic , artificial intelligence , partially observable markov decision process , neuro fuzzy , markov chain , artificial neural network , machine learning , mathematical optimization , markov process , markov model , fuzzy control system , mathematics , statistics , physics , quantum mechanics

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

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