# Toward Co-creative Dungeon Generation via Transfer Learning

arXiv: https://arxiv.org/abs/2107.12533 · HTML: https://mlsanity.com/paper/2107.12533/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 27 Jul 2021.

## Builds on (most cited first, 9 of 9)

- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [PCGRL: Procedural Content Generation via Reinforcement Learning](https://mlsanity.com/paper/2001.09212/) (2020)
- [The VGLC: The Video Game Level Corpus](https://mlsanity.com/paper/1606.07487/) (2016)
- [Friend, Collaborator, Student, Manager: How Design of an AI-Driven Game Level Editor Affects Creators](https://mlsanity.com/paper/1901.06417/) (2019)
- [Interactive Evolution and Exploration Within Latent Level-Design Space of Generative Adversarial Networks](https://mlsanity.com/paper/2004.00151/) (2020)
- [An Interaction Framework for Studying Co-Creative AI](https://mlsanity.com/paper/1903.09709/) (2019)
- [Co-Creative Level Design via Machine Learning](https://mlsanity.com/paper/1809.09420/) (2018)
- [Mixed-Initiative Level Design with RL Brush](https://mlsanity.com/paper/2008.02778/) (2020)
- [Generative Adversarial Network Rooms in Generative Graph Grammar Dungeons for The Legend of Zelda](https://mlsanity.com/paper/2001.05065/) (2020)

## Built on by (most cited first, 1 of 1)

- [Mechanic Maker: Accessible Game Development Via Symbolic Learning Program Synthesis](https://mlsanity.com/paper/2410.01096/) (2024)

## Abstract (arXiv)

Co-creative Procedural Content Generation via Machine Learning (PCGML) refers to systems where a PCGML agent and a human work together to produce output content. One of the limitations of co-creative PCGML is that it requires co-creative training data for a PCGML agent to learn to interact with humans. However, acquiring this data is a difficult and time-consuming process. In this work, we propose approximating human-AI interaction data and employing transfer learning to adapt learned co-creative knowledge from one game to a different game. We explore this approach for co-creative Zelda dungeon room generation.