Nearly optimal stationary strategies for the total reward Markov decision process

J. Wal, van der

doi:10.1007/978-3-642-68118-9_94

Nearly optimal stationary strategies for the total reward Markov decision process

J. Wal, van der

Stochastic Operations Research

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

Consider a Markov decision process with countable state space S and finite action space A. If in state i∈S action a∈A is taken then we get an immediate reward r(i,a) and the system makes a transition to state j with probability p(i,a,j). We assume Σj∈Sp(i,a,j)=1.

Original language	English
Title of host publication	DGOR
Subtitle of host publication	Papers of the Annual Meeting/Vorträge der Jahrestagung
Editors	G. Fandel, D. Fischer, H.C. Pfohl, K.P. Schuster, J. Schwarze
Place of Publication	Berlin
Publisher	Springer
Chapter	94
Pages	501-501
Number of pages	1
ISBN (Electronic)	978-3-642-68118-9
ISBN (Print)	3-540-10801-7, 978-3-540-10801-6
DOIs	https://doi.org/10.1007/978-3-642-68118-9_94
Publication status	Published - 1981
Event	9th Annual Meeting of DGOR - Essen, Germany Duration: 24 Sept 1980 → 26 Sept 1980

Publication series

Name	Operations Research Proceedings (ORP)
Volume	1980

Conference

Conference	9th Annual Meeting of DGOR
Country/Territory	Germany
City	Essen
Period	24/09/80 → 26/09/80

Access to Document

10.1007/978-3-642-68118-9_94

Cite this

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title = "Nearly optimal stationary strategies for the total reward Markov decision process",

abstract = "Consider a Markov decision process with countable state space S and finite action space A. If in state i∈S action a∈A is taken then we get an immediate reward r(i,a) and the system makes a transition to state j with probability p(i,a,j). We assume Σj∈Sp(i,a,j)=1.",

author = "\{Wal, van der\}, J.",

year = "1981",

doi = "10.1007/978-3-642-68118-9\_94",

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pages = "501--501",

editor = "G. Fandel and D. Fischer and H.C. Pfohl and K.P. Schuster and J. Schwarze",

booktitle = "DGOR",

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note = "9th Annual Meeting of DGOR ; Conference date: 24-09-1980 Through 26-09-1980",

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Wal, van der, J 1981, Nearly optimal stationary strategies for the total reward Markov decision process. in G Fandel, D Fischer, HC Pfohl, KP Schuster & J Schwarze (eds), DGOR: Papers of the Annual Meeting/Vorträge der Jahrestagung. Operations Research Proceedings (ORP), vol. 1980, Springer, Berlin, pp. 501-501, 9th Annual Meeting of DGOR, Essen, Germany, 24/09/80. https://doi.org/10.1007/978-3-642-68118-9_94

Nearly optimal stationary strategies for the total reward Markov decision process. / Wal, van der, J.
DGOR: Papers of the Annual Meeting/Vorträge der Jahrestagung. ed. / G. Fandel; D. Fischer; H.C. Pfohl; K.P. Schuster; J. Schwarze. Berlin: Springer, 1981. p. 501-501 (Operations Research Proceedings (ORP); Vol. 1980).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Nearly optimal stationary strategies for the total reward Markov decision process

AU - Wal, van der, J.

PY - 1981

Y1 - 1981

N2 - Consider a Markov decision process with countable state space S and finite action space A. If in state i∈S action a∈A is taken then we get an immediate reward r(i,a) and the system makes a transition to state j with probability p(i,a,j). We assume Σj∈Sp(i,a,j)=1.

AB - Consider a Markov decision process with countable state space S and finite action space A. If in state i∈S action a∈A is taken then we get an immediate reward r(i,a) and the system makes a transition to state j with probability p(i,a,j). We assume Σj∈Sp(i,a,j)=1.

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DO - 10.1007/978-3-642-68118-9_94

M3 - Conference contribution

SN - 3-540-10801-7

SN - 978-3-540-10801-6

T3 - Operations Research Proceedings (ORP)

SP - 501

EP - 501

BT - DGOR

A2 - Fandel, G.

A2 - Fischer, D.

A2 - Pfohl, H.C.

A2 - Schuster, K.P.

A2 - Schwarze, J.

PB - Springer

CY - Berlin

T2 - 9th Annual Meeting of DGOR

Y2 - 24 September 1980 through 26 September 1980

ER -

Nearly optimal stationary strategies for the total reward Markov decision process

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