  We learn recurrent neural network optimizers trained on simple synthetic
functions by gradient descent. We show that these learned optimizers exhibit a
remarkable degree of transfer in that they can be used to efficiently optimize
a broad range of derivative-free black-box functions, including Gaussian
process bandits, simple control objectives, global optimization benchmarks and
hyper-parameter tuning tasks. Up to the training horizon, the learned
optimizers learn to trade-off exploration and exploitation, and compare
favourably with heavily engineered Bayesian optimization packages for
hyper-parameter tuning.
