POMDPPlanners: Open-Source Package for POMDP Planning

2026-02-24

AI Summary

This work addresses the lack of support for scalability, reproducibility, and risk-sensitive scenarios in existing POMDP planning evaluation tools. To bridge this gap, we present an open-source Python framework that, for the first time in the open-source ecosystem, enables systematic empirical evaluation of safety-critical and risk-sensitive POMDPs. The framework integrates state-of-the-art planning algorithms, standardized benchmark environments, and automatic hyperparameter optimization via Optuna, while also incorporating persistent caching, fault recovery mechanisms, and configurable parallel simulation. Experimental results demonstrate that our tool substantially reduces computational overhead in large-scale simulations and significantly enhances both reproducibility and scalability, thereby filling a critical void in the landscape of risk-sensitive decision-making evaluation tools.

Technology Category

• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• POMDP
• planning
• risk-sensitive
• empirical evaluation
• decision-making under uncertainty

Innovation

• POMDP
• open-source
• risk-sensitive planning
• hyperparameter optimization
• parallel simulation