Speeding Up the Convergence of Value Iteration in Partially Observable Markov Decision Processes
| Autor: | Zhang, N. L., Zhang, W. |
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| Rok vydání: | 2011 |
| Předmět: | |
| Zdroj: | Journal Of Artificial Intelligence Research, Volume 14, pages 29-51, 2001 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1613/jair.761 |
| Popis: | Partially observable Markov decision processes (POMDPs) have recently become popular among many AI researchers because they serve as a natural model for planning under uncertainty. Value iteration is a well-known algorithm for finding optimal policies for POMDPs. It typically takes a large number of iterations to converge. This paper proposes a method for accelerating the convergence of value iteration. The method has been evaluated on an array of benchmark problems and was found to be very effective: It enabled value iteration to converge after only a few iterations on all the test problems. |
| Databáze: | arXiv |
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