POMDPPlanners: Open-Source Package for POMDP Planning

TL;DR

POMDPPlanners is an open-source Python toolkit that facilitates empirical evaluation of POMDP planning algorithms, supporting scalable, reproducible, and risk-sensitive decision-making research.

Contribution

It introduces a comprehensive, integrated package with advanced features like hyperparameter optimization, caching, and parallel simulation for POMDP planning research.

Findings

Supports a wide range of planning algorithms

Includes benchmark environments with safety-critical variants

Enhances research efficiency and reproducibility

Abstract

We present POMDPPlanners, an open-source Python package for empirical evaluation of Partially Observable Markov Decision Process (POMDP) planning algorithms. The package integrates state-of-the-art planning algorithms, a suite of benchmark environments with safety-critical variants, automated hyperparameter optimization via Optuna, persistent caching with failure recovery, and configurable parallel simulation -- reducing the overhead of extensive simulation studies. POMDPPlanners is designed to enable scalable, reproducible research on decision-making under uncertainty, with particular emphasis on risk-sensitive settings where standard toolkits fall short.