Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes

TL;DR

Incremental pruning is presented as the most efficient exact algorithm for solving POMDPs, offering a simple and fast approach based on dynamic programming with piecewise-linear convex value functions.

Contribution

The paper introduces and evaluates variations of the incremental pruning method, demonstrating its superior efficiency over previous algorithms for POMDPs.

Findings

Incremental pruning outperforms earlier algorithms in efficiency.

It is the most effective exact method for solving POMDPs.

The method simplifies the dynamic programming process for POMDPs.

Abstract

Most exact algorithms for general partially observable Markov decision processes (POMDPs) use a form of dynamic programming in which a piecewise-linear and convex representation of one value function is transformed into another. We examine variations of the "incremental pruning" method for solving this problem and compare them to earlier algorithms from theoretical and empirical perspectives. We find that incremental pruning is presently the most efficient exact method for solving POMDPs.