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Título
VQQL: a model to generalize in reinforcement learning
Autor
Facultad/Centro
Área de conocimiento
Datos de la obra
European Conference on Planning, Septiembre, 1999, Durham, Reino Unido
Fecha
1999-09-10
Abstract
Reinforcement learning har proven to be very successful for finding optimal policies on uncertian and/or dynamic domains. One of the problems on using such techniques appears with large state and action spaces. This problem appears very frequently given that most information in the type of tasks to which these techniques have been applied is continuous. In the paper, we describe a new mechanism for solving the states generalization problem in reinforcement learning algorithms, the VQQL technique
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Subtipo documental
info:eu-repo/semantics/lecture
URI
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