Name
Abstract | ||
|---|---|---|
This paper analyzes quasi-random sampling techniques for approximate dynamic programming. Specifically, low-discrepancy sequences and lattice point sets are investigated and compared as efficient schemes for uniform sampling of the state space in high-dimensional settings. The convergence analysis of the approximate solution is provided basing on geometric properties of the two discretization methods. It is also shown that such schemes are able to take advantage of regularities of the value functions, possibly through suitable transformations of the state vector. Simulation results concerning optimal management of a water reservoirs system and inventory control are presented to show the effectiveness of the considered techniques with respect to pure-random sampling. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/IJCNN.2013.6707065 | IJCNN |
Keywords | Field | DocType |
pure-random sampling,approximate dynamic programming,optimal management,random processes,discretization method,geometric property,convergence analysis,lattice point sets,state space,quasirandom sampling,water reservoir system,high-dimensional setting,sampling methods,dynamic programming,low-discrepancy sequences,inventory control,state vector | Slice sampling,Convergence (routing),Discretization,Dynamic programming,State vector,Mathematical optimization,Computer science,Sampling (statistics),State space,Nonuniform sampling | Conference |
ISSN | ISBN | Citations |
2161-4393 | 978-1-4673-6128-6 | 4 |
PageRank | References | Authors |
0.47 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cristiano Cervellera | 1 | 226 | 23.63 |
| Mauro Gaggero | 2 | 130 | 17.60 |
| Danilo Macciò | 3 | 64 | 10.95 |
| Roberto Marcialis | 4 | 9 | 2.36 |