POMDPPlanners: Open-Source Package for POMDP Planning
#POMDPPlanners #Partially Observable Markov Decision Process #Open-source Python package #AI research tools #Decision-making under uncertainty #Risk-sensitive settings #arXiv submission #Hyperparameter optimization
📌 Key Takeaways
- POMDPPlanners is an open-source Python package for evaluating POMDP planning algorithms
- The package integrates state-of-the-art planning algorithms and safety-critical benchmark environments
- It includes automated hyperparameter optimization and parallel simulation capabilities
- Designed specifically for risk-sensitive decision-making under uncertainty
- Aims to make AI research more scalable and reproducible
📖 Full Retelling
Researchers Yaacov Pariente and Vadim Indelman released POMDPPlanners, an open-source Python package for empirical evaluation of Partially Observable Markov Decision Process planning algorithms, on the arXiv preprint server on February 24, 2026, to address the lack of comprehensive tools for decision-making under uncertainty, particularly in risk-sensitive settings where existing toolkits fall short. The package represents a significant advancement in the field by integrating state-of-the-art planning algorithms with a suite of benchmark environments that include safety-critical variants, enabling researchers to test their approaches in more realistic scenarios. POMDPPlanners also incorporates automated hyperparameter optimization via Optuna, persistent caching with failure recovery, and configurable parallel simulation capabilities, which substantially reduce the computational overhead typically associated with extensive simulation studies in this domain. This comprehensive toolkit is designed to foster more scalable and reproducible research in artificial intelligence, particularly for applications where decision-making must account for partial information and significant risks.
🏷️ Themes
Artificial Intelligence, Open Source Software, Decision-Making Systems
📚 Related People & Topics
Hyperparameter optimization
Process of finding the optimal set of variables for a machine learning algorithm
In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts. Hyperparameter opt...
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--> Computer Science > Artificial Intelligence arXiv:2602.20810 [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 Abstract: We present POMDPPlanners, an open-source Python package for empirical evaluation of Partially Observable Markov Decision Process 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 and 1 other authors View PDF HTML TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-02 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps...
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