Risk-Sensitive Average Optimality in Markov Decision Chains

Karel Sladký (sladky@utia.cas.cz) and Raúl Montes- de-Oca (momr@xanum.uam.mx)
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Karel Sladký: Institute of Information Theory and Automation
Raúl Montes- de-Oca: Universidad Autónoma Metropolitana

A chapter in Operations Research Proceedings 2007, 2008, pp 69-74 from Springer

Abstract: Abstract We consider a Markov decision chain X = {X n, n = 0, 1, ...} with finite state space $$ \mathcal{I} $$ = {1, 2, ...,N} and a finite set $$ \mathcal{A}_i $$ = {1, 2, ...,K i} of possible decisions (actions) in state i ∈ $$ \mathcal{I} $$ . Supposing that in state i ∈ $$ \mathcal{I} $$ action k ∈ $$ \mathcal{A}_i $$ is selected, then state j is reached in the next transition with a given probability p ij k and one-stage transition reward r ij will be accrued to such transition.

Keywords: Markov Decision Process; Certainty Equivalent; Nonnegative Matrice; Exponential Utility; Finite State Space (search for similar items in EconPapers)
Date: 2008
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DOI: 10.1007/978-3-540-77903-2_11

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