| Title of paper:
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Generalized net model of neuro-dynamic programming algorithm
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| Author(s):
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| Tatiana Ilkova
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| Bioinformatics and Mathematical Modelling Department, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences
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| tanja@neftochim.bg
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| Olympia Roeva
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| Bioinformatics and Mathematical Modelling Department, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences
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| olympia@neftochim.bg
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| Mitko Petrov
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| Bioinformatics and Mathematical Modelling Department, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences
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| mpetrov@biomed.bas.bg
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| Juris Vanags
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| Laboratory of Bioengineering, Latvian State Institute of Wood Chemistry, Riga, Latvia
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| btc@edi.lv
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| Presented at:
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12th IWGN, Burgas, 17 June 2012
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| Published in:
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Conference proceedings, pages 17—26
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| Download:
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 PDF (73 Kb, File info)
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| Abstract:
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The apparatus of generalized nets (GN) is applied here to describe the algorithm of neuro-dynamic programming (NDP). NDP was an alternative to alleviate the “curse of dimensionality” of the Dynamic programming (DP). The traditional approach for solving the Bellman’s equation involves gridding of the state space, solving the optimization for each grid point, as well as performing the stagewise optimization until convergence is reached. The comprehensive sampling of state space can be avoided by identifying the relevant regions of the state space though simulation under judiciously chosen suboptimal policies, which is presented using NDP methods. The proposed method is particularly simple to implement and can be applied for on-line optimization.
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| Keywords:
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Generalized nets, Neuro-dynamic algorithm
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| AMS Classification:
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68Q85, 90C39.
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