Learning automata: an introduction
Learning automata: an introduction
Data mining with neural networks: solving business problems from application development to decision support
A reinforcement learning approach based on the fuzzy min-max neural network
Neural Processing Letters
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Reinforcement learning for an ART-based fuzzy adaptive learning control network
IEEE Transactions on Neural Networks
On the Computational Power of Max-Min Propagation Neural Networks
Neural Processing Letters
A neural network-based multi-agent classifier system
Neurocomputing
Approximation of stochastic processes by T--S fuzzy systems
Fuzzy Sets and Systems
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Grey reinforcement learning for incomplete information processing
TAMC'06 Proceedings of the Third international conference on Theory and Applications of Models of Computation
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The fuzzy min–max neural network constitutes a neural architecture that is based on hyperbox fuzzy sets and can be incrementally trained by appropriately adjusting the number of hyperboxes and their corresponding volumes. An extension to this network has been proposed recently, that is based on the notion of random hyperboxes and is suitable for reinforcement learning problems with discrete action space. In this work, we elaborate further on the random hyperbox idea and propose the stochastic fuzzy min–max neural network, where each hyperbox is associated with a stochastic learning automaton. Experimental results using the pole balancing problem indicate that the employment of this model as an action selection network in reinforcement learning schemes leads to superior learning performance compared with the traditional approach where the multilayer perceptron is employed.