  We introduce ES-MAML, a new framework for solving the model agnostic meta
learning (MAML) problem based on Evolution Strategies (ES). Existing algorithms
for MAML are based on policy gradients, and incur significant difficulties when
attempting to estimate second derivatives using backpropagation on stochastic
policies. We show how ES can be applied to MAML to obtain an algorithm which
avoids the problem of estimating second derivatives, and is also conceptually
simple and easy to implement. Moreover, ES-MAML can handle new types of
nonsmooth adaptation operators, and other techniques for improving performance
and estimation of ES methods become applicable. We show empirically that
ES-MAML is competitive with existing methods and often yields better adaptation
with fewer queries.
