Aprendizagem por Reforço Hierárquica e Computação Paralela Aplicada ao Problema dos Kservos
(Hierarchical Reinforcement Learning and Parallel Computing Applied to the kserver Problem)
Mademerson Leandro Costa (mademersonleandro@uern.br)^{1}, Carlos Alberto Araújo Padilha (caapadilha@inf.ufrgs.br)^{2}, Jorge Dantas Melo (jdmelo@dca.ufrn.br)^{3}, Adrião Duarte Dória Neto (adriao@dca.ufrn.br)^{3}
^{1}Universidade do Estado do Rio Grande do Norte ^{2}Universidade Federal do Rio Grande do Sul ^{3}Universidade Federal do Rio Grande do Norte
This paper appears in: Revista IEEE América Latina
Publication Date: Oct. 2016
Volume: 14, Issue: 10
ISSN: 15480992
Abstract:
In this paper was proposed an algorithm based on Hierarchical Reinforcement Learning (HRL) and Parallel Computing to solve an online computing problem, the KServer Problem (KSP). The size of the storage structure used for reinforcement learning to obtain the optimal policy grows exponentially with the number of states and actions, limiting its use to smaller problems due to the curse of dimensionality. The problem is modeled as a multiple steps decision process computed in parallel by applying the Qlearning algorithm to obtain optimal policies in a reduced number of nodes obtained from an clustering process. The results show the applicability of the proposed method to real problems of large size.
Index Terms:
Metrical Task Systems,The KServer Problem, Curse of Dimensionality, Hierarchical Reinforcement Learning, QLearning Algorithm, Parallel Computing.
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