# Tile Embedding: A General Representation for Procedural Level Generation via Machine Learning

arXiv: https://arxiv.org/abs/2110.03181 · HTML: https://mlsanity.com/paper/2110.03181/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 33 references that are in the map.
- First posted on arXiv 7 Oct 2021.

## Builds on (most cited first, 13 of 13)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Procedural Content Generation via Machine Learning (PCGML)](https://mlsanity.com/paper/1702.00539/) (2017)
- [Learning to Simulate Dynamic Environments with GameGAN](https://mlsanity.com/paper/2005.12126/) (2020)
- [The VGLC: The Video Game Level Corpus](https://mlsanity.com/paper/1606.07487/) (2016)
- [Super Mario as a String: Platformer Level Generation Via LSTMs](https://mlsanity.com/paper/1603.00930/) (2016)
- [Controllable Level Blending between Games using Variational Autoencoders](https://mlsanity.com/paper/2002.11869/) (2020)
- [Exploring Level Blending across Platformers via Paths and Affordances](https://mlsanity.com/paper/2009.06356/) (2020)
- [Autoencoder-augmented Neuroevolution for Visual Doom Playing](https://mlsanity.com/paper/1707.03902/) (2017)
- [Entity Embedding as Game Representation](https://mlsanity.com/paper/2010.01685/) (2020)
- [Explainable PCGML via Game Design Patterns](https://mlsanity.com/paper/1809.09419/) (2018)

## Built on by (most cited first, 9 of 9)

- [Pixel VQ-VAEs for Improved Pixel Art Representation](https://mlsanity.com/paper/2203.12130/) (2022)
- [GameTileNet: A Semantic Dataset for Low-Resolution Game Art in Procedural Content Generation](https://mlsanity.com/paper/2507.02941/) (2025)
- [tile2tile: Learning Game Filters for Platformer Style Transfer](https://mlsanity.com/paper/2208.07699/) (2022)
- [Narrative-to-Scene Generation: An LLM-Driven Pipeline for 2D Game Environments](https://mlsanity.com/paper/2509.04481/) (2025)
- [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)
- [Reconstructing Existing Levels through Level Inpainting](https://mlsanity.com/paper/2309.09472/) (2023)
- [Game Level Blending using a Learned Level Representation](https://mlsanity.com/paper/2306.16666/) (2023)
- [Representing and Generating Levels Over Time through Playtrace Reconstructive Partitioning](https://mlsanity.com/paper/2607.12097/) (2026)

## Abstract (arXiv)

In recent years, Procedural Level Generation via Machine Learning (PLGML) techniques have been applied to generate game levels with machine learning. These approaches rely on human-annotated representations of game levels. Creating annotated datasets for games requires domain knowledge and is time-consuming. Hence, though a large number of video games exist, annotated datasets are curated only for a small handful. Thus current PLGML techniques have been explored in limited domains, with Super Mario Bros. as the most common example. To address this problem, we present tile embeddings, a unified, affordance-rich representation for tile-based 2D games. To learn this embedding, we employ autoencoders trained on the visual and semantic information of tiles from a set of existing, human-annotated games. We evaluate this representation on its ability to predict affordances for unseen tiles, and to serve as a PLGML representation for annotated and unannotated games.