Explains how evolution strategies can optimize objectives where gradients are unavailable or unreliable, giving engineers a practical alternative to SGD for black-box or non-differentiable problems like RL reward shaping and hyperparameter tuning.
Key quotes
“Stochastic gradient descent is a universal choice for optimizing deep learning models.”
“With black-box optimization algorithms, you can evaluate a target function $f(x): \mathbb{R}^n \to \mathbb{R}$, even when you don’t know the precise analytic form of $f(x)$ and thus cannot compute gradients or the Hessian matrix.”
