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Título:
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Efficient learning of reactive robot behaviors with a Neural-Q_learning approach
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Autor/a:
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Carreras Pérez, Marc; Ridao Rodríguez, Pere; Batlle i Grabulosa, Joan; Nicosevici, Tudor
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Abstract:
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The purpose of this paper is to propose a Neural-Q_learning approach designed for online learning of simple and reactive robot behaviors. In this approach, the Q_function is generalized by a multi-layer neural network allowing the use of continuous states and actions. The algorithm uses a database of the most recent learning samples to accelerate and guarantee the convergence. Each Neural-Q_learning function represents an independent, reactive and adaptive behavior which maps sensorial states to robot control actions. A group of these behaviors constitutes a reactive control scheme designed to fulfill simple missions. The paper centers on the description of the Neural-Q_learning based behaviors showing their performance with an underwater robot in a target following task. Real experiments demonstrate the convergence and stability of the learning system, pointing out its suitability for online robot learning. Advantages and limitations are discussed |
Fecha de creación:
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17-05-2010 |
Materia(s):
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-Intel·ligència artificial -Robots mòbils -Xarxes neuronals (Informàtica) -Artificial intelligence -Neural networks (Computer science) -Mobile robots |
Derechos:
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Tots els drets reservats |
Tipo de documento:
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Artículo |
Editor:
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IEEE
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