The Linear Programming Approach to Approximate Dynamic Programming
The curse of dimensionality gives rise to prohibitive computational requirements that render infeasible the exact solution of large-scale stochastic control problems. We study an efficient method based on linear programming for approximating solutions to such problems. The approach "fits"...
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| Vydáno v: | Operations research Ročník 51; číslo 6; s. 850 - 865 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Linthicum
INFORMS
01.11.2003
Institute for Operations Research and the Management Sciences |
| Témata: | |
| ISSN: | 0030-364X, 1526-5463 |
| On-line přístup: | Získat plný text |
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| Abstract | The curse of dimensionality gives rise to prohibitive computational requirements that render infeasible the exact solution of large-scale stochastic control problems. We study an efficient method based on linear programming for approximating solutions to such problems. The approach "fits" a linear combination of pre-selected basis functions to the dynamic programming cost-to-go function. We develop error bounds that offer performance guarantees and also guide the selection of both basis functions and "state-relevance weights" that influence quality of the approximation. Experimental results in the domain of queueing network control provide empirical support for the methodology. |
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| AbstractList | The curse of dimensionality gives rise to prohibitive computational requirements that render infeasible the exact solution of large-scale stochastic control problems. We study an efficient method based on linear programming for approximating solutions to such problems. The approach "fits" a linear combination of pre-selected basis functions to the dynamic programming cost-to-go function. We develop error bounds that offer performance guarantees and also guide the selection of both basis functions and "state-relevance weights" that influence quality of the approximation. Experimental results in the domain of queueing network control provide empirical support for the methodology. The curse of dimensionality gives rise to prohibitive computational requirements that render infeasible the exact solution of large-scale stochastic control problems. We study an efficient method based on linear programming for approximating solutions to such problems. The approach fits a linear combination of pre-selected basis functions to the dynamic programming cost-to-go function. We develop error bounds that offer performance guarantees and also guide the selection of both basis functions and state-relevance weights that influence quality of the approximation. Experimental results in the domain of queueing network control provide empirical support for the methodology. [PUBLICATION ABSTRACT] |
| Audience | Trade |
| Author | Van Roy, B de Farias, D. P |
| Author_xml | – sequence: 1 fullname: de Farias, D. P – sequence: 2 fullname: Van Roy, B |
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| CODEN | OPREAI |
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| Snippet | The curse of dimensionality gives rise to prohibitive computational requirements that render infeasible the exact solution of large-scale stochastic control... |
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| SubjectTerms | Algorithms Approximation Approximation theory Average cost Dynamic programming Dynamic programming/optimal control: approximations/large-scale problems Error bounds Liapunov functions Linear programming Machine learning Mathematical vectors Methods Operations research Process controls Queueing networks Queues, algorithms: control of queueing networks Queuing Studies |
| Title | The Linear Programming Approach to Approximate Dynamic Programming |
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