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- Title
SOLUTION PROCEDURES FOR PARTIALLY OBSERVED MARKOV DECISION PROCESSES.
- Authors
White III, Chelsea C.; Scherer, William T.
- 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.
- Subjects
MARKOV processes; DECISION making; ALGORITHMS; STOCHASTIC convergence; MATHEMATICAL models; NUMERICAL analysis; APPROXIMATION theory; FUNCTIONAL analysis
- Publication
Operations Research, 1989, Vol 37, Issue 5, p791
- ISSN
0030-364X
- Publication type
Article
- DOI
10.1287/opre.37.5.791