DocumentCode
445893
Title
The k-server problem: a reinforcement learning approach
Author
Junior, M.L.L. ; Neto, Adrião D Doria ; Melo, Jorge D.
Author_Institution
Departamento de Engenharia de Computacao e Automacao, Univ. Fed. do Rio Grande do Norte, Natal, Brazil
Volume
2
fYear
2005
fDate
31 July-4 Aug. 2005
Firstpage
798
Abstract
This work presents an original algorithm, based on reinforcement learning, as a solution for the k-server problem. This problem was modelled as a multi-stage decision-making process, and subsequently the Q-learning algorithm was used as a means of solution. A series of experiments was conducted with the aim of evaluating the appropriateness and performance of the proposed solution. The results obtained show the efficiency of the suggested algorithm in comparison with other methods of solving the k-server problem frequently cited in the literature, whose rates of competitiveness have already been proven.
Keywords
decision making; learning (artificial intelligence); queueing theory; Q-learning algorithm; k-server problem; multi-stage decision-making process; reinforcement learning; Algorithm design and analysis; Costs; Decision making; Learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
Print_ISBN
0-7803-9048-2
Type
conf
DOI
10.1109/IJCNN.2005.1555954
Filename
1555954
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