  Since the debut of Evolution Strategies (ES) as a tool for Reinforcement
Learning by Salimans et al. 2017, there has been interest in determining the
exact relationship between the Evolution Strategies gradient and the gradient
of a similar class of algorithms, Finite Differences (FD).(Zhang et al. 2017,
Lehman et al. 2018) Several investigations into the subject have been
performed, investigating the formal motivational differences(Lehman et al.
2018) between ES and FD, as well as the differences in a standard benchmark
problem in Machine Learning, the MNIST classification problem(Zhang et al.
2017). This paper proves that while the gradients are different, they converge
as the dimension of the vector under optimization increases.
