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Efficient learning of reactive robot behaviors with a Neural-Q_learning approach
Carreras Pérez, Marc; Ridao Rodríguez, Pere; Batlle i Grabulosa, Joan; Nicosevici, Tudor
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
2010-05-17
Intel·ligència artificial
Robots mòbils
Xarxes neuronals (Informàtica)
Artificial intelligence
Neural networks (Computer science)
Mobile robots
Tots els drets reservats
Article
IEEE
         

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