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

TL;DR

This paper introduces a new method to significantly speed up the convergence of value iteration in POMDPs, making it more practical for complex planning under uncertainty.

Contribution

The paper proposes an acceleration technique for value iteration in POMDPs, reducing the number of iterations needed for convergence and improving computational efficiency.

Findings

Converges after fewer iterations on benchmark problems

Effective across diverse POMDP test cases

Enhances practical applicability of value iteration

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.