  The development of self-propelled particles at the micro- and the nanoscale
has sparked a huge potential for future applications in active matter physics,
microsurgery, and targeted drug delivery. However, while the latter
applications provoke the quest on how to optimally navigate towards a target,
such as e.g. a cancer cell, there is still no simple way known to determine the
optimal route in sufficiently complex environments. Here we develop a machine
learning-based approach that allows us, for the first time, to determine the
asymptotically optimal path of a self-propelled agent which can freely steer in
complex environments. Our method hinges on policy gradient-based deep
reinforcement learning techniques and, crucially, does not require any reward
shaping or heuristics. The presented method provides a powerful alternative to
current analytical methods to calculate optimal trajectories and opens a route
towards a universal path planner for future intelligent active particles.
