A Configurable Library for Generating and Manipulating Maze Datasets
Understanding how machine learning models respond to distributional shifts is
a key research challenge. Mazes serve as an excellent testbed due to varied
generation algorithms offering a nuanced platform to simulate both subtle and
pronounced distributional shifts. To enable systematic investigations of model
behavior on out-of-distribution data, we present $\texttt{maze-dataset}$, a
comprehensive library for generating, processing, and visualizing datasets
consisting of maze-solving tasks. With this library, researchers can easily
create datasets, having extensive control over the generation algorithm used,
the parameters fed to the algorithm of choice, and the filters that generated
mazes must satisfy. Furthermore, it supports multiple output formats, including
rasterized and text-based, catering to convolutional neural networks and
autoregressive transformer models. These formats, along with tools for
visualizing and converting between them, ensure versatility and adaptability in
research applications.