  Procedural content generation via machine learning (PCGML) has shown success
at producing new video game content with machine learning. However, the
majority of the work has focused on the production of static game content,
including game levels and visual elements. There has been much less work on
dynamic game content, such as game mechanics. One reason for this is the lack
of a consistent representation for dynamic game content, which is key for a
number of statistical machine learning approaches. We present an autoencoder
for deriving what we call "entity embeddings", a consistent way to represent
different dynamic entities across multiple games in the same representation. In
this paper we introduce the learned representation, along with some evidence
towards its quality and future utility.
