Good question. I'm not an RL expert, but those kind of models are sampling the possible space of game states, based on the rules of the game, to learn a winning

Good question. I'm not an RL expert, but those kind of models are sampling the possible space of game states, based on the rules of the game, to learn a winning policy. Complex games can have effectively infinite different states. What we're talking about here is approximating a distribution from a finite and comparatively small set of images. There's no "game" you can play to get new images, the training data is fixed. The analogy might be say sampling images from a computer animation, which shows the object your interested in in different plausible poses and backgrounds, lighting, angles. There's a question of making sure this generalizes to "real" images, but that can help. But it relies on the animators knowledge of what the object can look like in different poses. That's how information gets added.