Treffer: Q-Learning Algorithms with Random Truncation Bounds and Applications to Effective Parallel Computing
Title:
Q-Learning Algorithms with Random Truncation Bounds and Applications to Effective Parallel Computing
Authors:
Source:
Journal of optimization theory and applications. 137(2):435-451
Publisher Information:
New York, NY: Springer, 2008.
Publication Year:
2008
Physical Description:
print, 12 ref
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Recherche operationnelle. Gestion, Operational research. Management science, Recherche opérationnelle et modèles formalisés de gestion, Operational research and scientific management, Programmation mathématique, Mathematical programming, Algorithme apprentissage, Learning algorithm, Algoritmo aprendizaje, Algorithme parallèle, Parallel algorithm, Algoritmo paralelo, Algorithme récursif, Recursive algorithm, Algoritmo recursivo, Asynchrone, Asynchronous, Asincrono, Asynchronisation, Asynchronism, Asincronia, Convergence, Convergencia, Distribution charge, Load distribution, Distribución carga, Equilibrage charge, Load balancing, Equilibrio de carga, Méthode adaptative, Adaptive method, Método adaptativo, Méthode projection, Projection method, Método proyección, Méthode récursive, Recursive method, Método recursivo, Taux convergence, Convergence rate, Relación convergencia, Traitement signal, Signal processing, Procesamiento señal, Troncature, Truncation, Truncamiento, Q-learning, Rate of convergence, Recursive algorithms
Document Type:
Fachzeitschrift
Article
File Description:
text
Language:
English
Author Affiliations:
Department of Mathematics, Wayne State University, Detroit, MI, United States
Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI, United States
Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI, United States
ISSN:
0022-3239
Rights:
Copyright 2008 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Operational research. Management
Accession Number:
edscal.20339321
Database:
PASCAL Archive
Weitere Informationen
Motivated by an important problem of load balancing in parallel computing, this paper examines a modified algorithm to enhance Q-learning methods, especially in asynchronous recursive procedures for self-adaptive load distribution at run-time. Unlike the existing projection method that utilizes a fixed region, our algorithm employs a sequence of growing truncation bounds to ensure the boundedness of the iterates. Convergence and rates of convergence of the proposed algorithm are established. This class of algorithms has broad applications in signal processing, learning, financial engineering, and other related fields.