The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
The Linear Programming Approach to Approximate Dynamic Programming
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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Cites
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Cited by
Algorithms for Reinforcement Learning
Szepesvári, C. (2010) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.1007/978-3-031-01551-9 · Cited by 1,049
Iyengar, G. N. (2005) · Mathematics of Operations Research · doi:10.1287/moor.1040.0129 · Cited by 566
Dynamic Multipriority Patient Scheduling for a Diagnostic Resource
Patrick, J., Puterman, M. L., & Queyranne, M. (2008) · Operations Research · doi:10.1287/opre.1080.0590 · Cited by 377
On Constraint Sampling in the Linear Programming Approach to Approximate Dynamic Programming
de Farias, D. P., & Van Roy, B. (2004) · Mathematics of Operations Research · doi:10.1287/moor.1040.0094 · Cited by 373
Self-citationDynamic Programming and Suboptimal Control: A Survey from ADP to MPC*
Bertsekas, D. P. (2005) · European Journal of Control · doi:10.3166/ejc.11.310-334 · Cited by 366
Metadata
- Authors
- de Farias, D. P., & Van Roy, B.
- Venue
- Operations Research
- Published
- 2003-12
- Volume
- 51
- Issue
- 6
- Pages
- 850-865
- ISSN
- 0030-364X (ISSN-L)
- 1526-5463
- OpenAlex
- W1745373831
- Cited by
- 687
- References
- 30
- FWCI
- 20.76
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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