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An Iterative Aggregation Procedure for Markov Decision Processes

Author

Listed:
  • Roy Mendelssohn

    (National Marine Fisheries Service, NOAA, Honolulu, Hawaii)

Abstract

An iterative aggregation procedure is described for solving large scale, finite state, finite action Markov decision processes (MDPs). At each iteration, an aggregate master problem and a sequence of smaller subproblems are solved. The weights used to form the aggregate master problem are based on the estimates from the previous iteration. Each subproblem is a finite state, finite action MDP with a reduced state space and unequal row sums. Global convergence of the algorithm is proven under very weak assumptions. The proof relates this technique to other iterative methods that have been suggested for general linear programs.

Suggested Citation

  • Roy Mendelssohn, 1982. "An Iterative Aggregation Procedure for Markov Decision Processes," Operations Research, INFORMS, vol. 30(1), pages 62-73, February.
    • Handle: RePEc:inm:oropre:v:30:y:1982:i:1:p:62-73
    DOI: 10.1287/opre.30.1.62
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    Cited by:

    1. Benjamin Van Roy, 2006. "Performance Loss Bounds for Approximate Value Iteration with State Aggregation," Mathematics of Operations Research, INFORMS, vol. 31(2), pages 234-244, May.
    2. Jie Ning & Matthew J. Sobel, 2019. "Easy Affine Markov Decision Processes," Operations Research, INFORMS, vol. 67(6), pages 1719-1737, November.
    3. Chevalier, Philippe & Lamas, Alejandro & Lu, Liang & Mlinar, Tanja, 2015. "Revenue management for operations with urgent orders," European Journal of Operational Research, Elsevier, vol. 240(2), pages 476-487.
    4. Ali Fattahi & Sriram Dasu & Reza Ahmadi, 2023. "Peak-Load Energy Management by Direct Load Control Contracts," Management Science, INFORMS, vol. 69(5), pages 2788-2813, May.
    5. Michael Z. Spivey & Warren B. Powell, 2004. "The Dynamic Assignment Problem," Transportation Science, INFORMS, vol. 38(4), pages 399-419, November.

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