POMDPPlanners: Open-Source Package for POMDP Planning
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866910031460433920 |
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| author | Pariente, Yaacov Indelman, Vadim |
| author_facet | Pariente, Yaacov Indelman, Vadim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_20810 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | POMDPPlanners: Open-Source Package for POMDP Planning Pariente, Yaacov Indelman, Vadim Artificial Intelligence 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. |
| title | POMDPPlanners: Open-Source Package for POMDP Planning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2602.20810 |