  I demonstrate the potential of reinforcement learning (RL) to prepare quantum
states of strongly periodically driven non-linear single-particle models. The
ability of Q-Learning to control systems far away from equilibrium is exhibited
by steering the quantum Kapitza oscillator to the Floquet-engineered stable
inverted position in the presence of a strong periodic drive within several
shaking cycles. The study reveals the potential of the intra-period
(micromotion) dynamics, often neglected in Floquet engineering, to take
advantage over pure stroboscopic control at moderate drive frequencies. Without
any knowledge about the underlying physical system, the algorithm is capable of
learning solely from tried protocols and directly from simulated noisy quantum
measurement data, and is stable to noise in the initial state, and sources of
random failure events in the control sequence. Model-free RL can provide new
insights into automating experimental setups for out-of-equilibrium systems
undergoing complex dynamics, with potential applications in quantum
information, quantum optics, ultracold atoms, trapped ions, and condensed
matter.

Quantum Gases;Other Condensed Matter;Statistical Mechanics;Computational Physics;Quantum Physics