How to use Noise to your advantage ? – Towards Data Science

Recent works have shown that, by allowing some inaccuracy when training deep neural networks, not only the training performance but also the accuracy of the model can be improved. Neural networks are capable of learning output functions that can change wildly with small changes in input. Adding noise to inputs randomly is like telling the network to not change the output in a ball around your exact input. By limiting the amount of information in a network, we force it to learn compact representations of input features. RL is an area of machine learning that assumes there is an agent situated in an environment.