This paper presents Natural Evolution Strategies (NES), a recent family of
algorithms that constitute a more principled approach to black-box optimization
than established evolutionary algorithms. NES maintains a parameterized
distribution on the set of solution candidates, and the natural gradient is
used to update the distribution's parameters in the direction of higher
expected fitness. We introduce a collection of techniques that address issues
of convergence, robustness, sample complexity, computational complexity and
sensitivity to hyperparameters.