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The Planner Optimization Problem: Formulations and Frameworks

arXiv 2303.06768 ↗First posted 12 Mar 2023cs.AI cs.ROcited by 0

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Abstract (arXiv)

Identifying internal parameters for planning is crucial to maximizing the performance of a planner. However, automatically tuning internal parameters which are conditioned on the problem instance is especially challenging. A recent line of work focuses on learning planning parameter generators, but lack a consistent problem definition and software framework. This work proposes the unified planner optimization problem (POP) formulation, along with the Open Planner Optimization Framework (OPOF), a highly extensible software framework to specify and to solve these problems in a reusable manner.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 31 references that are in the map. First posted on arXiv 12 Mar 2023.

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10 of 31 references.

References · 10

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2021SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimizationcited by 230
  3. 2016DESPOT: Online POMDP Planning with Regularizationcited by 106
  4. 2014ParamILS: An Automatic Algorithm Configuration Frameworkcited by 89
  5. 2016Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Sciencecited by 75
  6. 2020MAGIC: Learning Macro-Actions for Online POMDP Planningcited by 17
  7. 2021MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasetscited by 16
  8. 2018Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networkscited by 10
  9. 2022LEADER: Learning Attention over Driving Behaviors for Planning under Uncertaintycited by 9
  10. 2022PyPop7: A Pure-Python Library for Population-Based Black-Box Optimizationcited by 7

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