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Efficient learning of reactive robot behaviors with a Neural-Q_learning approach

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

© IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002, vol. 1, p. 1020-1025

IEEE

Author: Carreras Pérez, Marc
Ridao Rodríguez, Pere
Batlle i Grabulosa, Joan
Nicosevici, Tudor
Date: 2002
Abstract: 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
Format: application/pdf
Citation: Carreras, M., Ridao, P. , Batlle, J., i Nicosevici, T. (2002). Efficient learning of reactive robot behaviors with a Neural-Q_learning approach. IEEE/RSJ International Conference on Intelligent Robots and Systems, 1, 1020-1025. Recuperat 05 maig 2010, a http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1041525
ISBN: 0-7803-7398-7
Document access: http://hdl.handle.net/10256/2163
Language: eng
Publisher: IEEE
Collection: Reproducció digital del document publicat a: http://dx.doi.org/10.1109/IRDS.2002.1041525
Articles publicats (D-ATC)
Is part of: © IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002, vol. 1, p. 1020-1025
Rights: Tots els drets reservats
Subject: Intel·ligència artificial
Robots mòbils
Xarxes neuronals (Informàtica)
Artificial intelligence
Neural networks (Computer science)
Mobile robots
Title: Efficient learning of reactive robot behaviors with a Neural-Q_learning approach
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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