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Bibliographic Details
Main Authors: Bäuerle, Nicole, Jaśkiewicz, Anna
Format: Preprint
Published: 2023
Subjects:
Online Access:https://arxiv.org/abs/2311.06896
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author Bäuerle, Nicole
Jaśkiewicz, Anna
author_facet Bäuerle, Nicole
Jaśkiewicz, Anna
contents 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
format Preprint
id arxiv_https___arxiv_org_abs_2311_06896
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Markov Decision Processes with Risk-Sensitive Criteria: An Overview
Bäuerle, Nicole
Jaśkiewicz, Anna
Risk Management
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
title Markov Decision Processes with Risk-Sensitive Criteria: An Overview
topic Risk Management
url https://arxiv.org/abs/2311.06896