  Having access to accurate game state information is of utmost importance for
any artificial intelligence task including game-playing, testing, player
modeling, and procedural content generation. Self-Supervised Learning (SSL)
techniques have shown to be capable of inferring accurate game state
information from the high-dimensional pixel input of game footage into
compressed latent representations. Contrastive Learning is a popular SSL
paradigm where the visual understanding of the game's images comes from
contrasting dissimilar and similar game states defined by simple image
augmentation methods. In this study, we introduce a new game scene augmentation
technique -- named GameCLR -- that takes advantage of the game-engine to define
and synthesize specific, highly-controlled renderings of different game states,
thereby, boosting contrastive learning performance. We test our GameCLR
technique on images of the CARLA driving simulator environment and compare it
against the popular SimCLR baseline SSL method. Our results suggest that
GameCLR can infer the game's state information from game footage more
accurately compared to the baseline. Our proposed approach allows us to conduct
game artificial intelligence research by directly utilizing screen pixels as
input.
