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We consider a robust approach to address uncertainty in model parameters in Markov Decision Processes (MDPs), which are widely used to model dynamic optimization in many applications.
Markov decision processes with uncertain transition probabilities
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Lectures on modern convex optimization: analysis, algorithms, and engineering applications , volume 2
Aharon Ben-Tal and Arkadi Nemirovski · 2001
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Recursive multiple-priors
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Robust control of Markov decision processes with uncertain transition probabilities
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Andrew J Schaefer, Matthew D Bailey, Steven M Shechter, and Mark S Roberts · 2005
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Dimitris Bertsimas and Aurélie Thiele · 2006
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S. Mannor, O. Mebel, and H. Xu · 2016
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Lauren N Steimle and Brian T Denton · 2017
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State aggregation learning from Markov transition data
Yaqi Duan, Zheng Tracy Ke, and Mengdi Wang · 2018
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Data uncertainty in Markov chains: Application to cost-effectiveness analyses of medical innovations
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Fast bellman updates for robust MDPs
C.P. Ho, M. Petrik, and W.Wiesemann · 2018
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The operator approach to entropy games
Marianne Akian, Stéphane Gaubert, Julien Grand-Clément, and Jérémie Guillaud · 2019
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