Discounted and average Markov decision processes with unbounded rewards: New conditions
type: Item
instance of: scholarly article
title: Discounted and average Markov decision processes with unbounded rewards: New conditions
zbMATH Open document ID: 0777.90076
DOI: 10.1016/0022-247X(92)90379-R
author: Qiying Hu
published in: Journal of Mathematical Analysis and Applications
publication date: 1993-04-01T00:00:00Z
review text: For Markov decision processes (MDP) with discrete time and countable state space, conditions are given under which the known optimality equation for the discounted and for the average case, resp., has a unique resp. a bounded solution and which also regard unbounded rewards. The conditions are weaker than those given by \textit{J. M. Harrison} [Ann. Math. Statist. 43, No. 2, 636-644 (1972; Zbl 0262.90064)], resp. by \textit{S. M. Ross} [Ann. Math. Statist. 39, No. 2, 412-423 (1968; Zbl 0157.505)]. Because these new conditions are difficult to verify, further sufficient, recursively operating conditions are given. In the last part of the paper, continuous-time MDP are investigated by these methods and reduced to discrete-time ones. The paper contains a rich material so that only essentially new lemmas and theorems are proved. Also not all used symbols are explicitely defined. However, many remarks permit the reader to make connections to previously known facts.
Mathematics Subject Classification ID: 90C40
zbMATH DE Number: 150681
zbMATH Keywords: discounted case
zbMATH Keywords: discrete time
zbMATH Keywords: countable state space
zbMATH Keywords: average case
MaRDI profile type: Publication
cites work: Non-Discounted Denumerable Markovian Decision Models
cites work: Discrete Dynamic Programming with Unbounded Rewards
cites work: Q3908791
cites work: CTMDP and its relationship with DTMDP
full work available at URL: 0022-247x(92)90379-r
OpenAlex ID: W2043959499
description: scientific article; zbMATH DE number 150681