  In this work we introduce the application of black-box quantum control as an
interesting rein- forcement learning problem to the machine learning community.
We analyze the structure of the reinforcement learning problems arising in
quantum physics and argue that agents parameterized by long short-term memory
(LSTM) networks trained via stochastic policy gradients yield a general method
to solving them. In this context we introduce a variant of the proximal policy
optimization (PPO) algorithm called the memory proximal policy optimization
(MPPO) which is based on this analysis. We then show how it can be applied to
specific learning tasks and present results of nu- merical experiments showing
that our method achieves state-of-the-art results for several learning tasks in
quantum control with discrete and continouous control parameters.
