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