maze-dataset: Maze Generation with Algorithmic Variety and Representational Flexibility

Python Jupyter Notebook Submitted 09 April 2025 • Published 21 October 2025
Software repository Paper review Download paper Software archive
Review

Editor: @ekourlit (all papers)
Reviewers: @amcandio (all reviews), @Jemoka (all reviews)

Authors

Michael Igorevich Ivanitskiy (0000-0002-4213-4993), Aaron Sandoval (0009-0002-8380-6140), Alexander F. Spies (0000-0002-8708-1530), Tilman Räuker (0009-0009-6321-4413), Brandon Knutson (0009-0004-8413-0239), Cecilia Diniz Behn (0000-0002-8078-5105), Samy Wu Fung (0000-0002-2926-4582)

Citation

Ivanitskiy et al., (2025). maze-dataset: Maze Generation with Algorithmic Variety and Representational Flexibility. Journal of Open Source Software, 10(114), 8633, https://doi.org/10.21105/joss.08633

@article{Ivanitskiy2025, doi = {10.21105/joss.08633}, url = {https://doi.org/10.21105/joss.08633}, year = {2025}, publisher = {The Open Journal}, volume = {10}, number = {114}, pages = {8633}, author = {Ivanitskiy, Michael Igorevich and Sandoval, Aaron and Spies, Alexander F. and Räuker, Tilman and Knutson, Brandon and Behn, Cecilia Diniz and Fung, Samy Wu}, title = {maze-dataset: Maze Generation with Algorithmic Variety and Representational Flexibility}, journal = {Journal of Open Source Software} }
Copy citation string · Copy BibTeX  
Tags

machine learning distributional shift maze generation datasets

Altmetrics
Markdown badge

 

License

Authors of JOSS papers retain copyright.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Creative Commons License

Journal of Open Source Software is an affiliate of the Open Source Initiative.

Journal of Open Source Software is part of Open Journals, which is a NumFOCUS-sponsored project.

Table of Contents
Public user content licensed CC BY 4.0 unless otherwise specified.
ISSN 2475-9066