  Artificial bacteria flagella (ABFs) are magnetic helical micro-robots that
can be remotely controlled via a uniform, rotating magnetic field. While the
control of a single swimmer with the uniform magnetic field is straightforward,
the independent control of multiple ABFs is an unsolved problem. We demonstrate
such independent control by exploiting the distinct geometries of the ABFs
through an analytical solution and a reinforcement learning algorithm. We
compare the two algorithms and demonstrate the superiority of reinforcement
learning in achieving multiple objectives. The results demonstrate, for the
first time, effective independent navigation of realistic micro-swimmers in a
viscous flow field. We argue that the present precise control strategies are
readily applicable in the laboratory.
