Path planning of magnetic microswimmers in high-fidelity simulations of
  capillaries with deep reinforcement learning
Lucas Amoudruz Sergey Litvinov Petros Koumoutsakos
Biomedical applications such as targeted drug delivery, microsurgery or
sensing rely on reaching precise areas within the body in a minimally invasive
way. Artificial bacterial flagella (ABFs) have emerged as potential tools for
this task by navigating through the circulatory system. While the control and
swimming characteristics of ABFs is understood in simple scenarios, their
behavior within the bloodstream remains unclear. We conduct simulations of ABFs
evolving in the complex capillary networks found in the human retina. The ABF
is robustly guided to a prescribed target by a reinforcement learning agent
previously trained on a reduced order model.