Local Linear Regression and Low-Discrepancy Sampling for Approximate Dynamic Programming (Abstract/Poster in atti di convegno)

Type
Label
  • Local Linear Regression and Low-Discrepancy Sampling for Approximate Dynamic Programming (Abstract/Poster in atti di convegno) (literal)
Anno
  • 2014-01-01T00:00:00+01:00 (literal)
Alternative label
  • C. Cervellera; V. C P. Chen; D. Macciò (2014)
    Local Linear Regression and Low-Discrepancy Sampling for Approximate Dynamic Programming
    in Informs Annual Meeting, San Francisco, Novembre, 9-12
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • C. Cervellera; V. C P. Chen; D. Macciò (literal)
Note
  • Abstract (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 1) Institute of Intelligent Systems for Automation, National Research Council, Via De Marini 6, 16149, Genova, Italy; 2) Center on Stochastic Modeling, Optimization & Statistics, University of Texas at Arlington, Campus Box 19017, Arlington, TX 76019-0017, USA; 3) Institute of Intelligent Systems for Automation, National Research Council, Via De Marini 6, 16149, Genova, Italy (literal)
Titolo
  • Local Linear Regression and Low-Discrepancy Sampling for Approximate Dynamic Programming (literal)
Abstract
  • Abstract: Approximate Dynamic Programming is the standard method for the numerical solution of the well-known Bellman's equations. With such technique, two main issues arise: (i) the choice of a class of models to approximate the value functions; (ii) the definition of an efficient sampling of the domain where estimates of the value functions are computed. In this work the use of local linear regression based models is investigated when low-discrepancy sampling methods are used to sample the state space. (literal)
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