  Robust and high-precision quantum control is crucial but challenging for
scalable quantum computation and quantum information processing. Traditional
adiabatic control suffers severe limitations on gate performance imposed by
environmentally induced noise because of a quantum system's limited coherence
time. In this work, we experimentally demonstrate an alternative approach {to
quantum control} based on deep reinforcement learning (DRL) on a trapped
$^{171}\mathrm{Yb}^{+}$ ion. In particular, we find that DRL leads to fast and
robust {digital quantum operations with running time bounded by shortcuts to
adiabaticity} (STA). Besides, we demonstrate that DRL's robustness against both
Rabi and detuning errors can be achieved simultaneously without any input from
STA. Our experiments reveal a general framework of digital quantum control,
leading to a promising enhancement in quantum information processing.
