  Microswimmers can acquire information on the surrounding fluid by sensing
mechanical queues. They can then navigate in response to these signals. We
analyse this navigation by combining deep reinforcement learning with direct
numerical simulations to resolve the hydrodynamics. We study how local and
non-local information can be used to train a swimmer to achieve particular
swimming tasks in a non-uniform flow field, in particular a zig-zag shear flow.
The swimming tasks are (1) learning how to swim in the vorticity direction, (2)
the shear-gradient direction, and (3) the shear flow direction. We find that
access to lab frame information on the swimmer's instantaneous orientation is
all that is required in order to reach the optimal policy for (1,2). However,
information on both the translational and rotational velocities seem to be
required to achieve (3). Inspired by biological microorganisms we also consider
the case where the swimmers sense local information, i.e. surface hydrodynamic
forces, together with a signal direction. This might correspond to gravity or,
for micro-organisms with light sensors, a light source. In this case, we show
that the swimmer can reach a comparable level of performance as a swimmer with
access to lab frame variables. We also analyse the role of different swimming
modes, i.e. pusher, puller, and neutral swimmers.
