  On-screen game footage contains rich contextual information that players
process when playing and experiencing a game. Learning pixel representations of
games can benefit artificial intelligence across several downstream tasks
including game-playing agents, procedural content generation, and player
modelling. The generalizability of these methods, however, remains a challenge,
as learned representations should ideally be shared across games with similar
game mechanics. This could allow, for instance, game-playing agents trained on
one game to perform well in similar games with no re-training. This paper
explores how generalizable pre-trained computer vision encoders can be for such
tasks, by decomposing the latent space into content embeddings and style
embeddings. The goal is to minimize the domain gap between games of the same
genre when it comes to game content critical for downstream tasks, and ignore
differences in graphical style. We employ a pre-trained Vision Transformer
encoder and a decomposition technique based on game genres to obtain separate
content and style embeddings. Our findings show that the decomposed embeddings
achieve style invariance across multiple games while still maintaining strong
content extraction capabilities. We argue that the proposed decomposition of
content and style offers better generalization capacities across game
environments independently of the downstream task.
