I understand that L1 and L2 regularization helps to prevent overfitting. My question is then, does that mean they also help a neural network learn faster as a result?
The way I'm thinking is that since the regularization techniques reduce weights (to 0 or close to 0 depending on whether it's L1 or L2) that are not important to the neural network, this would, in turn, result in "better values" for the output neurons right? Or perhaps I am completely wrong.
For example, suppose I have a neural network that is to train a snake to move around a NxN environment. With regularization, the snake will learn faster in terms of survive longer in the game?