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
Link To Document :
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