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

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Author(s): Toygar Karadeniz | Levent Akin

Journal: International Journal of Advanced Robotic Systems
ISSN 1729-8806

Volume: 1;
Issue: 4;
Date: 2008;
Original page

Keywords: POMDP | neuro-fuuzy | rule-based systems

ABSTRACT
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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