  We apply a reinforcement learning algorithm to show how smart particles can
learn approximately optimal strategies to navigate in complex flows. In this
paper we consider microswimmers in a paradigmatic three-dimensional case given
by a stationary superposition of two Arnold-Beltrami-Childress flows with
chaotic advection along streamlines. In such a flow, we study the evolution of
point-like particles which can decide in which direction to swim, while keeping
the velocity amplitude constant. We show that it is sufficient to endow the
swimmers with a very restricted set of actions (six fixed swimming directions
in our case) to have enough freedom to find efficient strategies to move upward
and escape local fluid traps. The key ingredient is the
learning-from-experience structure of the algorithm, which assigns positive or
negative rewards depending on whether the taken action is, or is not,
profitable for the predetermined goal in the long term horizon. This is another
example supporting the efficiency of the reinforcement learning approach to
learn how to accomplish difficult tasks in complex fluid environments.
