Adaptive value function approximation for continuous-state stochastic dynamic programming

Approximate dynamic programming (ADP) commonly employs value function approximation to numerically solve complex dynamic programming problems. A statistical perspective of value function approximation employs a design and analysis of computer experiments (DACE) approach, where the “computer experime...

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Vydané v:	Computers & operations research Ročník 40; číslo 4; s. 1076 - 1084
Hlavní autori:	Fan, Huiyuan, Tarun, Prashant K., Chen, Victoria C.P.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Kidlington Elsevier Ltd 01.04.2013 Elsevier Pergamon Press Inc
Predmet:	Applied sciences Approximate dynamic programming Approximation Design of experiments Dynamic programming Exact sciences and technology Experimental design Inventory control, production control. Distribution Inventory forecasting Mathematical analysis Mathematical models Mathematical programming Mathematics Neural network Neural networks Number theoretic methods Operational research and scientific management Operational research. Management science Probability and statistics Sample size Samples Sciences and techniques of general use Sequential design of experiments Statistical analysis Statistical methods Statistical modeling Statistics Stochastic models Studies Approximate dynamic programming Statistical modeling Inventory forecasting Neural network Number theoretic methods Sequential design of experiments Sample size State space Function approximation Modeling Adaptive method Discretization Sequential method Inventory control Dynamic programming Feedforward Statistical analysis Sequential design Computer simulation Continuous function State space method Stochastic programming Approximation method Experimental design Complex programming Number theory
ISSN:	0305-0548, 1873-765X, 0305-0548
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