Abstract
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.
This work has been partially funded by the Spanish Ministry of Science and Technology, project DPI2006-15346-C03-02.
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Martín H., J.A., de Lope, J. (2007). A k-NN Based Perception Scheme for Reinforcement Learning. In: Moreno Díaz, R., Pichler, F., Quesada Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2007. EUROCAST 2007. Lecture Notes in Computer Science, vol 4739. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75867-9_18
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DOI: https://doi.org/10.1007/978-3-540-75867-9_18
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