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

In: Stochastic Analysis, Filtering, and Stochastic Optimization

Author

Listed:
  • 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)

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.

Suggested Citation

  • Tomasz R. Bielecki & Tao Chen & Igor Cialenco, 2022. "Risk-Sensitive Markov Decision Problems under Model Uncertainty: Finite Time Horizon Case," Springer Books, in: George Yin & Thaleia Zariphopoulou (ed.), Stochastic Analysis, Filtering, and Stochastic Optimization, pages 33-52, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-98519-6_2
    DOI: 10.1007/978-3-030-98519-6_2
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