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

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.

Keywords

• POMDP
• neuro-fuuzy
• rule-based systems