POMDPPlanners: Open-Source Package for POMDP Planning
This package enables scalable, reproducible research on decision-making under uncertainty, particularly for risk-sensitive settings where existing toolkits are inadequate.
The authors tackled the challenge of evaluating POMDP planning algorithms by developing POMDPPlanners, an open-source Python package that integrates state-of-the-art algorithms, benchmark environments, and tools like hyperparameter optimization and parallel simulation, resulting in reduced overhead for simulation studies.
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.