  The ability to prepare a physical system in a desired quantum state is
central to many areas of physics such as nuclear magnetic resonance, cold
atoms, and quantum computing. Yet, preparing states quickly and with high
fidelity remains a formidable challenge. In this work we implement cutting-edge
Reinforcement Learning (RL) techniques and show that their performance is
comparable to optimal control methods in the task of finding short,
high-fidelity driving protocol from an initial to a target state in
non-integrable many-body quantum systems of interacting qubits. RL methods
learn about the underlying physical system solely through a single scalar
reward (the fidelity of the resulting state) calculated from numerical
simulations of the physical system. We further show that quantum state
manipulation, viewed as an optimization problem, exhibits a spin-glass-like
phase transition in the space of protocols as a function of the protocol
duration. Our RL-aided approach helps identify variational protocols with
nearly optimal fidelity, even in the glassy phase, where optimal state
manipulation is exponentially hard. This study highlights the potential
usefulness of RL for applications in out-of-equilibrium quantum physics.
