Solution procedures for partially observed Markov decision processes

Solution procedures for partially observed Markov decision processes

0.00 Avg rating0 Votes
Article ID: iaor1990285
Country: United States
Volume: 37
Issue: 5
Start Page Number: 791
End Page Number: 797
Publication Date: Sep 1989
Journal: Operations Research
Authors: ,
Keywords: markov processes
Abstract:

The authors 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 that potentially significant computational value of these algorithms.

Reviews

Required fields are marked *. Your email address will not be published.