  Evolutionary Strategies (ES) are known to be an effective black-box
optimization technique for deep neural networks when the true gradients cannot
be computed, such as in Reinforcement Learning. We continue a recent line of
research that uses surrogate gradients to improve the gradient estimation of
ES. We propose a novel method to optimally incorporate surrogate gradient
information. Our approach, unlike previous work, needs no information about the
quality of the surrogate gradients and is always guaranteed to find a descent
direction that is better than the surrogate gradient. This allows to
iteratively use the previous gradient estimate as surrogate gradient for the
current search point. We theoretically prove that this yields fast convergence
to the true gradient for linear functions and show under simplifying
assumptions that it significantly improves gradient estimates for general
functions. Finally, we evaluate our approach empirically on MNIST and
reinforcement learning tasks and show that it considerably improves the
gradient estimation of ES at no extra computational cost.

Neural and Evolutionary Computing;Machine Learning