Reward delay is not very significant in this task, since the task is episodic and fully observable, and there is no time preference, thus you can just play a game to completion without updating and then assign the final reward to all the positions. In more general reinforcement learning settings, where you want to update your policy during the execution, you have to use some kind of temporal difference learning method, which is further complicated if the world states are not fully observable. Credit assignment is taken care of by backpropagation, as usual in neural networks. I don't know why RaelwayScot brought it up, unless they meant something else.