# The Planner Optimization Problem: Formulations and Frameworks

arXiv: https://arxiv.org/abs/2303.06768 · HTML: https://mlsanity.com/paper/2303.06768/

## 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.

## Builds on (most cited first, 10 of 10)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization](https://mlsanity.com/paper/2109.09831/) (2021)
- [DESPOT: Online POMDP Planning with Regularization](https://mlsanity.com/paper/1609.03250/) (2016)
- [ParamILS: An Automatic Algorithm Configuration Framework](https://mlsanity.com/paper/1401.3492/) (2014)
- [Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science](https://mlsanity.com/paper/1603.06212/) (2016)
- [MAGIC: Learning Macro-Actions for Online POMDP Planning](https://mlsanity.com/paper/2011.03813/) (2020)
- [MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets](https://mlsanity.com/paper/2112.06402/) (2021)
- [Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1807.07362/) (2018)
- [LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty](https://mlsanity.com/paper/2209.11422/) (2022)
- [PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization](https://mlsanity.com/paper/2212.05652/) (2022)

## 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.