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Markov decision processes with risk-sensitive criteria: an overview

Author

Listed:
  • Nicole Bäuerle

    (Karlsruhe Institute of Technology (KIT))

  • Anna Jaśkiewicz

    (Wrocław University of Science and Technology)

Abstract

The paper provides an overview of the theory and applications of risk-sensitive Markov decision processes. The term ’risk-sensitive’ refers here to the use of the Optimized Certainty Equivalent as a means to measure expectation and risk. This comprises the well-known entropic risk measure and Conditional Value-at-Risk. We restrict our considerations to stationary problems with an infinite time horizon. Conditions are given under which optimal policies exist and solution procedures are explained. We present both the theory when the Optimized Certainty Equivalent is applied recursively as well as the case where it is applied to the cumulated reward. Discounted as well as non-discounted models are reviewed.

Suggested Citation

  • Nicole Bäuerle & Anna Jaśkiewicz, 2024. "Markov decision processes with risk-sensitive criteria: an overview," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 99(1), pages 141-178, April.
    • Handle: RePEc:spr:mathme:v:99:y:2024:i:1:d:10.1007_s00186-024-00857-0
    DOI: 10.1007/s00186-024-00857-0
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