Risk-Sensitive Markov Decision Problems under Model Uncertainty: Finite Time Horizon Case

Tomasz R. Bielecki (), Tao Chen () and Igor Cialenco ()
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Tomasz R. Bielecki: Illinois Institute of Technology, Department of Applied Mathematics
Tao Chen: University of Michigan, Department of Mathematics
Igor Cialenco: Illinois Institute of Technology, Department of Applied Mathematics

A chapter in Stochastic Analysis, Filtering, and Stochastic Optimization, 2022, pp 33-52 from Springer

Abstract: Abstract In this paper we study a class of risk-sensitive Markovian control problems in discrete time subject to model uncertainty. We consider a risk-sensitive discounted cost criterion with finite time horizon. The used methodology is the one of adaptive robust control combined with machine learning.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-98519-6_2

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DOI: 10.1007/978-3-030-98519-6_2

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