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

Author

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  • Arruda, E.F.
  • Fragoso, M.D.

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.

Suggested Citation

  • Arruda, E.F. & Fragoso, M.D., 2015. "Solving average cost Markov decision processes by means of a two-phase time aggregation algorithm," European Journal of Operational Research, Elsevier, vol. 240(3), pages 697-705.
    • Handle: RePEc:eee:ejores:v:240:y:2015:i:3:p:697-705
    DOI: 10.1016/j.ejor.2014.08.023
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    References listed on IDEAS

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    1. Arruda, Edilson F. & Ourique, Fabrício O. & LaCombe, Jason & Almudevar, Anthony, 2013. "Accelerating the convergence of value iteration by using partial transition functions," European Journal of Operational Research, Elsevier, vol. 229(1), pages 190-198.
    2. Arruda, E.F. & do Val, J.B.R., 2008. "Stability and optimality of a multi-product production and storage system under demand uncertainty," European Journal of Operational Research, Elsevier, vol. 188(2), pages 406-427, July.
    3. Zhang, Jiaqi & Archibald, Thomas W., 2011. "A semi-Markov model with holdout transshipment policy and phase-type exponential lead time," European Journal of Operational Research, Elsevier, vol. 211(3), pages 650-657, June.
    4. Arruda, E.F. & Fragoso, M.D. & do Val, J.B.R., 2011. "Approximate dynamic programming via direct search in the space of value function approximations," European Journal of Operational Research, Elsevier, vol. 211(2), pages 343-351, June.
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