Evolution Strategies: Almost Embarrassingly Parallel Optimization

I watched Ilya Sutskever's talk on their new evolutionary strategies paper. The reason this paper is fascinating is that they use a relatively dumb, simple stochastic method of optimisation that shouldn't really work well in practice, and show that it is actually competitive with SGD/back-propagation-based methods in RL. This is mainly due to the fact that it parallelizes so naturally. Evolution strategies (ES) is can be best described as a gradient descent method which uses gradients estimated from stochastic perturbations around the current parameter value. While the authors did comparisons in the context of RL, and there are many RL-specific advantages, here I'm focussing on ES as a general black-box optimisation method.