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POMDPPlanners: Open-Source Package for POMDP Planning

Yaacov Pariente, Vadim Indelman

TL;DR

POMDPPlanners is an open-source Python package for empirical evaluation of Partially Observable Markov Decision Process planning algorithms, designed to enable scalable, reproducible research on decision-making under uncertainty, with particular emphasis on risk-sensitive settings where standard toolkits fall short.

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.

POMDPPlanners: Open-Source Package for POMDP Planning

TL;DR

POMDPPlanners is an open-source Python package for empirical evaluation of Partially Observable Markov Decision Process planning algorithms, designed to enable scalable, reproducible research on decision-making under uncertainty, with particular emphasis on risk-sensitive settings where standard toolkits fall short.

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.
Paper Structure (10 sections, 1 figure, 1 table)

This paper contains 10 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Architecture
  3. Core Abstractions
  4. Workflows and Experiment Management
  5. Benchmark Environments and Algorithms
  6. Benchmark Environments
  7. Planning Algorithms
  8. Usage Example
  9. Conclusions.
  10. Availability.

Figures (1)

  • Figure 1: Architecture of POMDPPlanners. Workflows feed into a shared Task Manager with persistent caching. Dashed arrows indicate data flow between abstractions; outputs are logged via MLflow.