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arXiv · 1106.0251

Speeding Up the Convergence of Value Iteration in Partially Observable Markov Decision Processes

N. L. Zhang·W. Zhang
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Abstract

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.

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N. L. Zhang, W. Zhang. 2011-06-01. Speeding Up the Convergence of Value Iteration in Partially Observable Markov Decision Processes. https://doi.org/10.1613/jair.761

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