Article

A Reinforcement Learning Approach Based on the Fuzzy Min-Max Neural Network.

University of Ioannina; National Technical University of Athens
Neural Processing Letters (Impact Factor: 1.24). 01/1996; 4:167-172. DOI: 10.1007/BF00426025
Source: DBLP

ABSTRACT 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. Two versions have been proposed: for supervised and unsupervised learning. In this paper a modified approach is presented that is appropriate for reinforcement learning problems with discrete action space and is applied to the difficult task of autonomous vehicle navigation when no a priori knowledge of the enivronment is available. Experimental results indicate that the proposed reinforcement learning network exhibits superior learning behavior compared to conventional reinforcement schemes.

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    ABSTRACT: 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. Keywords: Fuzzy min-max neural network, reinforcement learning, stochastic automaton, pole balancing pro...
    Neural Processing Letters 09/2000; · 1.24 Impact Factor