Nevin L. Zhang firstname.lastname@example.org
Weihong Zhang email@example.com
Department of Computer Science
Hong Kong University of Science & Technology
Clear Water Bay Road, Kowloon, Hong Kong, CHINA
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.