Descripción
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A perception scheme for Reinforcement Learning (RL) is developed as a function approximator. The main motivation for the development of this scheme is the need for generalization when the problem to be solved has continuous state variables. We propose a solution to the generalization problem in RL algorithms using a k-nearest-neighbor pattern classification (k-NN). By means of the k-NN technique we investigate the effect of collective decision making as a mechanism of perception and action-selection and a sort of back-propagation of its proportional influence in the action-selection process as the factor that moderate the learning of each decision making unit. A very well known problem is presented as a case study to illustrate the results of this k-NN based perception scheme. | |
Internacional
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Si |
DOI
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Edición del Libro
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0 |
Editorial del Libro
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Springer |
ISBN
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978-3-540-75866-2 |
Serie
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4739 |
Título del Libro
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Computer Aided Systems Theory ¿ EUROCAST 2007 |
Desde página
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138 |
Hasta página
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145 |