we get into something like computer vision for example so the reason computer vision works so well is that for every single input frame you have a target label and this lets you do very efficient gradient descent with something like back propagation whereas in a reinforcement learning setting you're having to deal with this very big problem of sparse reward setting and this is why you know computer vision is showing some very impressive results while something is simple as stacking one block onto another seems very difficult even for