Finite-Memory Suboptimal Design for Partially Observed Markov Decision Processes

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

Operations Research, 1994, vol. 42, issue 3, 439-455

Abstract: We develop bounds on the value function and a suboptimal design for the partially observed Markov decision process. These bounds and suboptimal design are based on the M most recent observations and actions. An a priori measure of the quality of these bounds is given. We show that larger M implies tighter bounds. An operations count analysis indicates that ( # A # Z ) M +1 ( # S ) multiplications and additions are required per successive approximations iteration of the suboptimal design algorithm, where A , Z , and S are the action, observation, and state spaces, respectively, suggesting the algorithm is of potential use for problems with large state spaces. A preliminary numerical study indicates that the quality of the suboptimal design can be excellent.

Keywords: dynamic; programming:; Markov; decision; processes (search for similar items in EconPapers)
Date: 1994
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