  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.
