  Meta-learning traditionally relies on backpropagation through entire tasks to
iteratively improve a model's learning dynamics. However, this approach is
computationally intractable when scaled to complex tasks. We propose a
distributed evolutionary meta-learning strategy using Tensor Processing Units
(TPUs) that is highly parallel and scalable to arbitrarily long tasks with no
increase in memory cost. Using a Prototypical Network trained with evolution
strategies on the Omniglot dataset, we achieved an accuracy of 98.4% on a
5-shot classification problem. Our algorithm used as much as 40 times less
memory than automatic differentiation to compute the gradient, with the
resulting model achieving accuracy within 1.3% of a backpropagation-trained
equivalent (99.6%). We observed better classification accuracy as high as 99.1%
with larger population configurations. We further experimentally validate the
stability and performance of ES-ProtoNet across a variety of training
conditions (varying population size, model size, number of workers, shot, way,
ES hyperparameters, etc.). Our contributions are twofold: we provide the first
assessment of evolutionary meta-learning in a supervised setting, and create a
general framework for distributed evolution strategies on TPUs.
