Reinforcement learning has a problem in that it gets very little labelled data. You may have a million frames, but the only label is the score. Which may only c

Reinforcement learning has a problem in that it gets very little labelled data. You may have a million frames, but the only label is the score. Which may only change a few times per game. Training the net to predict the next frame is sort of unsupervised learning. It can learn the rules of the game without score information at all. The second thing is that RL is different than prediction. Even if you can predict the next frame exactly, finding the optimal set of moves is still a hard problem. The algorithm needs to learn more than just predicting what will happen, but also what the optimal action is in every situation. That is something that can be practiced in simulations, or "dreams".