  Learning to Optimize is a recently proposed framework for learning
optimization algorithms using reinforcement learning. In this paper, we explore
learning an optimization algorithm for training shallow neural nets. Such
high-dimensional stochastic optimization problems present interesting
challenges for existing reinforcement learning algorithms. We develop an
extension that is suited to learning optimization algorithms in this setting
and demonstrate that the learned optimization algorithm consistently
outperforms other known optimization algorithms even on unseen tasks and is
robust to changes in stochasticity of gradients and the neural net
architecture. More specifically, we show that an optimization algorithm trained
with the proposed method on the problem of training a neural net on MNIST
generalizes to the problems of training neural nets on the Toronto Faces
Dataset, CIFAR-10 and CIFAR-100.
