Machine Learning, Optimization, Gradient Descent, Reinforcement Learning.
is analogous to using L2 regularization for neural networks. As we increase the regularization, we increase the bias but reduce the variance of our model weights. And from what we know about L2 regularization, there’s usually a sweet spot where the trade off between bias and variance gives us