  As the length scales of the smallest technology continue to advance beyond
the micron scale it becomes increasingly important to equip robotic components
with the means for intelligent and autonomous decision making with limited
information. With the help of a tabular Q-learning algorithm, we design a model
for training a microswimmer, to navigate quickly through an environment given
by various different scalar motility fields, while receiving a limited amount
of local information. We compare the performances of the microswimmer, defined
via time of first passage to a target, with performances of suitable reference
cases. We show that the strategy obtained with our reinforcement learning model
indeed represents an efficient navigation strategy, that outperforms the
reference cases. By confronting the swimmer with a variety of unfamiliar
environments after the finalised training, we show that the obtained strategy
generalises to different classes of random fields.
