Solution Procedures for Partially Observed Markov Decision Processes

Chelsea C. White and William T. Scherer
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Chelsea C. White: University of Virginia, Charlottesville, Virginia
William T. Scherer: University of Virginia, Charlottesville, Virginia

Operations Research, 1989, vol. 37, issue 5, 791-797

Abstract: We present three algorithms to solve the infinite horizon, expected discounted total reward partially observed Markov decision process (POMDP). Each algorithm integrates a successive approximations algorithm for the POMDP due to A. Smallwood and E. Sondik with an appropriately generalized numerical technique that has been shown to reduce CPU time until convergence for the completely observed case. The first technique is reward revision. The second technique is reward revision integrated with modified policy iteration. The third is a standard extrapolation. A numerical study indicates the potentially significant computational value of these algorithms.

Keywords: dynamic programming: Markov; finite state (search for similar items in EconPapers)
Date: 1989
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