Approximate receding horizon approach for Markov decision processes: average reward case
type: Item
instance of: scholarly article
title: Approximate receding horizon approach for Markov decision processes: average reward case
zbMATH Open document ID: 1064.90051
DOI: 10.1016/S0022-247X(03)00506-7
author: Hyeong Soo Chang
author: Steven I. Marcus
published in: Journal of Mathematical Analysis and Applications
publication date: 2003-11-20T00:00:00Z
review text: The authors consider an approximation scheme for solving Markov decision processes (MDPs) with countable state space, finite action space, and bounded rewards that uses an approximate solution of a fixed finite-horizon sub-MDP of a given infinite-horizon MDP to create a stationary policy, which they call ''approximate receding horizon control''. They analyze the performance of the approximate receding horizon control in some conditions, study two examples, also provide a simple proof on the policy improvement for countable state space, and discuss practical implementations of these schemes via simulation.
Mathematics Subject Classification ID: 90C40
Mathematics Subject Classification ID: 91B06
zbMATH DE Number: 2006348
zbMATH Keywords: Markov decision process
zbMATH Keywords: receding horizon control
zbMATH Keywords: Infinite-horizon average reward
zbMATH Keywords: policy improvement
zbMATH Keywords: ergodicity
reviewed by: Mariano Ruiz Espejo
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 2006348