Ítem


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

Autor: Carreras Pérez, Marc
Ridao Rodríguez, Pere
Batlle i Grabulosa, Joan
Nicosevici, Tudor
Data: 2002
Resum: 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
Cita: 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
Accés al document: http://hdl.handle.net/10256/2163
Llenguatge: eng
Editor: IEEE
Col·lecció: Reproducció digital del document publicat a: http://dx.doi.org/10.1109/IRDS.2002.1041525
Articles publicats (D-ATC)
És part de: © IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002, vol. 1, p. 1020-1025
Drets: Tots els drets reservats
Matèria: Intel·ligència artificial
Robots mòbils
Xarxes neuronals (Informàtica)
Artificial intelligence
Neural networks (Computer science)
Mobile robots
Títol: Efficient learning of reactive robot behaviors with a Neural-Q_learning approach
Tipus: info:eu-repo/semantics/article
Repositori: DUGiDocs

Matèries

Autors