Scalable grid‐based approximation algorithms for partially observable Markov decision processes

Abstract Partially observable Markov decision processes (POMDPs) are a well‐established sequential decision making framework. Once a problem is modeled using this framework, a suitable POMDP solution algorithm is employed to obtain a policy that guides the user throughout the decision making process. However, POMDPs are notoriously difficult to solve to optimality. Therefore, there exist many approximate solution algorithms that are designed to generate policies for large‐scale POMDP models. On the other hand, many such approaches lack performance guarantees in terms of the solution quality. In this article, we focus on exact solution methods as well as approximate methods that provide bounds on the optimal value. Specifically, we investigate the performance improvements for the POMDP solution algorithms obtained through distributed implementations. We provide a detailed empirical analysis on various test problems, which highlights the benefits of the proposed approach.