  The range and speed of direct ink writing, the workhorse of 3d and 4d
printing, is limited by the practice of liquid extrusion from a nozzle just
above the surface to prevent instabilities to cause deviations from the
required print path. But what if could harness the ``fluid rope trick", whence
a thin stream of viscous fluid falling from a height spontaneously folds or
coils, to write specified patterns on a substrate? Using Deep Reinforcement
Learning we control the motion of the extruding nozzle and thence the fluid
patterns that are deposited on the surface. The learner (nozzle) repeatedly
interacts with the environment (a viscous filament simulator), and improves its
strategy using the results of this experience. We demonstrate the results in an
experimental setting where the learned motion control instructions are used to
drive a viscous jet to accomplish complex tasks such as cursive writing and
Pollockian paintings.
