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[2602.20810] POMDPPlanners: Open-Source Package for POMDP Planning
Summary
POMDPPlanners is an open-source Python package designed for the empirical evaluation of POMDP planning algorithms, integrating advanced features for scalable research in decision-making under uncertainty.
Why It Matters
This package addresses the need for effective tools in POMDP planning, particularly in safety-critical environments. By providing a comprehensive suite of algorithms and benchmarking environments, it enhances reproducibility and scalability in research, which is crucial for advancing AI decision-making capabilities.
Key Takeaways
- POMDPPlanners integrates state-of-the-art POMDP planning algorithms.
- It features automated hyperparameter optimization and persistent caching.
- The package is tailored for risk-sensitive decision-making scenarios.
- It supports configurable parallel simulations to enhance research efficiency.
- Designed for reproducible research, it fills gaps left by standard toolkits.
Computer Science > Artificial Intelligence arXiv:2602.20810 (cs) [Submitted on 24 Feb 2026] Title:POMDPPlanners: Open-Source Package for POMDP Planning Authors:Yaacov Pariente, Vadim Indelman View a PDF of the paper titled POMDPPlanners: Open-Source Package for POMDP Planning, by Yaacov Pariente and 1 other authors View PDF HTML (experimental) 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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.20810 [cs.AI] (or arXiv:2602.20810v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.20810 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yaacov Pariente [view email] [v1] Tue, 24 Feb 2026 11:50:04 UTC (12 KB) Full-text links: Access Paper: View a PDF of the paper titled POMDPPlanners: Open-Source Package for POMDP Planning, by Yaacov Pariente ...
