# The VGLC: The Video Game Level Corpus

arXiv: https://arxiv.org/abs/1606.07487 · HTML: https://mlsanity.com/paper/1606.07487/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2016.

## Built on by (most cited first, 30 of 41)

- [Large Language Models and Games: A Survey and Roadmap](https://mlsanity.com/paper/2402.18659/) (2024)
- [Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap](https://mlsanity.com/paper/2401.10034/) (2024)
- [MarioGPT: Open-Ended Text2Level Generation through Large Language Models](https://mlsanity.com/paper/2302.05981/) (2023)
- [Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencoder](https://mlsanity.com/paper/2102.12463/) (2021)
- [Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros Study](https://mlsanity.com/paper/2106.15877/) (2021)
- [Illuminating the Space of Beatable Lode Runner Levels Produced By Various Generative Adversarial Networks](https://mlsanity.com/paper/2101.07868/) (2021)
- [Games for Artificial Intelligence Research: A Review and Perspectives](https://mlsanity.com/paper/2304.13269/) (2023)
- [Dungeon and Platformer Level Blending and Generation using Conditional VAEs](https://mlsanity.com/paper/2106.12692/) (2021)
- [Tile Embedding: A General Representation for Procedural Level Generation via Machine Learning](https://mlsanity.com/paper/2110.03181/) (2021)
- [10 Years of the PCG workshop: Past and Future Trends](https://mlsanity.com/paper/2104.11037/) (2021)
- [Lode Encoder: AI-constrained co-creativity](https://mlsanity.com/paper/2308.01312/) (2023)
- [Mario Plays on a Manifold: Generating Functional Content in Latent Space through Differential Geometry](https://mlsanity.com/paper/2206.00106/) (2022)
- [Hybrid Encoding For Generating Large Scale Game Level Patterns With Local Variations](https://mlsanity.com/paper/2105.12960/) (2021)
- [Pixel VQ-VAEs for Improved Pixel Art Representation](https://mlsanity.com/paper/2203.12130/) (2022)
- [Using Multiple Generative Adversarial Networks to Build Better-Connected Levels for Mega Man](https://mlsanity.com/paper/2102.00337/) (2021)
- [Generating Lode Runner Levels by Learning Player Paths with LSTMs](https://mlsanity.com/paper/2107.12532/) (2021)
- [Semantic Loss Functions for Neuro-Symbolic Structured Prediction](https://mlsanity.com/paper/2405.07387/) (2024)
- [Procedural Content Generation using Behavior Trees (PCGBT)](https://mlsanity.com/paper/2107.06638/) (2021)
- [Online Game Level Generation from Music](https://mlsanity.com/paper/2207.05271/) (2022)
- [tile2tile: Learning Game Filters for Platformer Style Transfer](https://mlsanity.com/paper/2208.07699/) (2022)
- [Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games](https://mlsanity.com/paper/2302.08479/) (2023)
- [Procedural Content Generation via Knowledge Transformation (PCG-KT)](https://mlsanity.com/paper/2305.00644/) (2023)
- [Active Learning for Classifying 2D Grid-Based Level Completability](https://mlsanity.com/paper/2309.04367/) (2023)
- [Guided Game Level Repair via Explainable AI](https://mlsanity.com/paper/2410.23101/) (2024)
- [Ensemble Learning For Mega Man Level Generation](https://mlsanity.com/paper/2107.12524/) (2021)
- [Toward Co-creative Dungeon Generation via Transfer Learning](https://mlsanity.com/paper/2107.12533/) (2021)
- [On Linking Level Segments](https://mlsanity.com/paper/2203.05057/) (2022)
- [Clustering-based Tile Embedding (CTE): A General Representation for Level Design with Skewed Tile Distributions](https://mlsanity.com/paper/2210.12789/) (2022)
- [Joint Level Generation and Translation Using Gameplay Videos](https://mlsanity.com/paper/2306.16662/) (2023)
- [Lode Enhancer: Level Co-creation Through Scaling](https://mlsanity.com/paper/2308.01543/) (2023)

## Abstract (arXiv)

Levels are a key component of many different video games, and a large body of work has been produced on how to procedurally generate game levels. Recently, Machine Learning techniques have been applied to video game level generation towards the purpose of automatically generating levels that have the properties of the training corpus. Towards that end we have made available a corpora of video game levels in an easy to parse format ideal for different machine learning and other game AI research purposes.