  We apply the technique of reinforcement learning to the control of nonlinear
matter waves. In this method, an agent controls the position, strength, and
shape of an external Gaussian potential to create and manipulate quantized
vortices in a Bose-Einstein condensate (BEC) trapped in a harmonic potential.
The density and velocity distributions of the BEC at each moment obtained by
the Gross-Pitaevskii evolution are directly input into a convolutional neural
network to determine the next action of the agent. We demonstrate that a
stationary single-vortex state can be produced in a two-dimensional system, and
a stationary vortex-ring state can be produced in a three-dimensional system.
