Contractive Approximations in Risk-Sensitive Average Semi-Markov Decision Chains on a Finite State Space

This work concerns with semi-Markov decision chains evolving on a finite state space. The controller has a positive and constant risk sensitivity coefficient, and the performance of a control policy is measured by the risk-sensitive average cost criterion. Under conditions ensuring that the optimal...

Full description

Saved in:
Bibliographic Details
Published in:Journal of optimization theory and applications 2022, Vol.192 (1), p.271-291
Main Authors: Camilo-Garay, Carlos, Cavazos-Cadena, Rolando, Cruz-Suárez, Hugo
Format: Article
Language:English
Subjects:
Applications of Mathematics
Approximation
Calculus of Variations and Optimal Control
Optimization
Chains
Engineering
Fixed points (mathematics)
Markov chains
Mathematics
Mathematics and Statistics
Operations Research/Decision Theory
Operators
Optimization
Theory of Computation
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This work concerns with semi-Markov decision chains evolving on a finite state space. The controller has a positive and constant risk sensitivity coefficient, and the performance of a control policy is measured by the risk-sensitive average cost criterion. Under conditions ensuring that the optimal value function is determined via a single optimality equation, the fixed points of a family of contractive operators are used to obtain convergent approximations to the optimal average cost and to a solution of optimality equation, extending the classical discounted approach to the context of the paper. In contrast with the Markovian case, the contractive operators utilized in this work depend on two parameters.
ISSN:0022-3239
1573-2878
DOI:10.1007/s10957-021-01968-y