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Robust Dynamic Programming
Abstract
In this paper we propose a robust formulation for discrete time dynamic programming (DP). The objective of the robust formulation is to systematically mitigate the sensitivity of the DP optimal policy to ambiguity in the underlying transition probabilities. The ambiguity is modeled by associating a set of conditional measures with each state-action pair. Consequently, in the robust formulation each policy has a set of measures associated with it. We prove that when this set of measures has a certain “rectangularity” property, all of the main results for finite and infinite horizon DP extend to natural robust counterparts. We discuss techniques from Nilim and El Ghaoui [17] for constructing suitable sets of conditional measures that allow one to efficiently solve for the optimal robust policy. We also show that robust DP is equivalent to stochastic zero-sum games with perfect information.
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Cites
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 36,808
Markov decision processes: Discrete stochastic dynamic programming
Hazeghi, K., & Puterman, M. L. (1995) · Journal of the American Statistical Association · doi:10.2307/2291177 · Cited by 8,037
“Risk, Ambiguity, and the Savage Axioms.”
Ellsberg, D. (1961) · The Quarterly Journal of Economics · doi:10.2307/1884324 · Cited by 7,891
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Puterman, M. L. (1994) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316887 · Cited by 6,024
Maxmin expected utility with non-unique prior
Gilboa, I., & Schmeidler, D. (1989) · Journal of Mathematical Economics · doi:10.1016/0304-4068(89)90018-9 · Cited by 4,040
(2012) · Encyclopedia of the Sciences of Learning · doi:10.1007/978-1-4419-1428-6_5086 · Cited by 3,515
Ben-Tal, A., & Nemirovski, A. (1998) · Mathematics of Operations Research · doi:10.1287/moor.23.4.769 · Cited by 2,598
Robust Control and Model Uncertainty
Hansen, L. P., & Sargent, T. J. (2001) · American Economic Review · doi:10.1257/aer.91.2.60 · Cited by 1,344
(2011) · Encyclopedia of Machine Learning · doi:10.1007/978-0-387-30164-8_588 · Cited by 950
Robust Portfolio Selection Problems
Goldfarb, D., & Iyengar, G. (2003) · Mathematics of Operations Research · doi:10.1287/moor.28.1.1.14260 · Cited by 895
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Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016) · arXiv · doi:10.48550/arxiv.1606.06565 · Cited by 1,314
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Conservative Q-Learning for Offline Reinforcement Learning
Kumar, A., Zhou, A., Tucker, G., & Levine, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.04779 · Cited by 543
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Robust Markov Decision Processes
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Metadata
- Authors
- Iyengar, G. N.
- Venue
- Mathematics of Operations Research
- Published
- 2005-05
- Volume
- 30
- Issue
- 2
- Pages
- 257-280
- ISSN
- 0364-765X (ISSN-L)
- 1526-5471
- OpenAlex
- W2168565265
- Cited by
- 566
- References
- 20
- FWCI
- 15.69
- 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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