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Solving average cost Markov decision processes by means of a two-phase time aggregation algorithm

Solving average cost Markov decision processes by means of a two-phase time aggregation algorithm
Solving average cost Markov decision processes by means of a two-phase time aggregation algorithm

This paper introduces a two-phase approach to solve average cost Markov decision processes, which is based on state space embedding or time aggregation. In the first phase, time aggregation is applied for policy optimization in a prescribed subset of the state space, and a novel result is applied to expand the evaluation to the whole state space. This evaluation is then used in the second phase in a policy improvement step, and the two phases are then alternated until convergence is attained. Some numerical experiments illustrate the results.

Dynamic programming, Embedding, Markov decision processes, Stochastic optimal control, Time aggregation
0377-2217
697-705
Arruda, E. F.
8eb3bd83-e883-4bf3-bfbc-7887c5daa911
Fragoso, M. D.
7f484139-de97-4458-aa6b-dc3249811a08
Arruda, E. F.
8eb3bd83-e883-4bf3-bfbc-7887c5daa911
Fragoso, M. D.
7f484139-de97-4458-aa6b-dc3249811a08

Arruda, E. F. and Fragoso, M. D. (2015) Solving average cost Markov decision processes by means of a two-phase time aggregation algorithm. European Journal of Operational Research, 240 (3), 697-705. (doi:10.1016/j.ejor.2014.08.023).

Record type: Article

Abstract

This paper introduces a two-phase approach to solve average cost Markov decision processes, which is based on state space embedding or time aggregation. In the first phase, time aggregation is applied for policy optimization in a prescribed subset of the state space, and a novel result is applied to expand the evaluation to the whole state space. This evaluation is then used in the second phase in a policy improvement step, and the two phases are then alternated until convergence is attained. Some numerical experiments illustrate the results.

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More information

Published date: 1 February 2015
Keywords: Dynamic programming, Embedding, Markov decision processes, Stochastic optimal control, Time aggregation

Identifiers

Local EPrints ID: 446040
URI: http://eprints.soton.ac.uk/id/eprint/446040
ISSN: 0377-2217
PURE UUID: 1cf1528f-040e-4ac4-bc43-b831062ae521
ORCID for E. F. Arruda: ORCID iD orcid.org/0000-0002-9835-352X

Catalogue record

Date deposited: 19 Jan 2021 17:33
Last modified: 09 Jan 2022 04:11

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Contributors

Author: E. F. Arruda ORCID iD
Author: M. D. Fragoso

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