One really cool aspect of this: For the actual game state representation that the model would receive as input, we found that passing an "image" encoding to th

One really cool aspect of this: For the actual game state representation that the model would receive as input, we found that passing an "image" encoding to the CNN resulted in the best performance, beating all benchmark procedural agents and other types of networks (e.g. fully connected). It sounds like they literally decompose the game screen into inputs. E.g. in FPS games, your health is often displayed in the exact same spot; it passes a screenshot of that into the network. Or maybe not. Maybe they decode the game state into an "image" in the sense that health is represented as a single pixel that ranges from RGB 0,0,0 to RGB 255,255,255. That would make more sense, but it's also slightly less exciting. Theoretically the model should be able to infer what health means simply by having enough experience, even if it's a more complex representation like Arabic numerals rather than a handcrafted input. Anyone know if there are any other details, like model weights (ha ha, not likely), an architecture diagram, a paper, or some code snippet from some prototype that the researchers used for inspiration? The "image" representation is really quite interesting to me, since I hadn't thought about feeding data to networks that way. Theoretically a GAN could learn a thing or two about the world from having this sort of "image" input too.