  The behavior of living systems is based on the experience they gained through
their interactions with the environment [1]. This experience is stored in the
complex biochemical networks of cells and organisms to provide a relationship
between a sensed situation and what to do in this situation [2-4]. An
implementation of such processes in artificial systems has been achieved
through different machine learning algorithms [5, 6]. However, for microscopic
systems such as artificial microswimmers which mimic propulsion as one of the
basic functionalities of living systems [7, 8] such adaptive behavior and
learning processes have not been implemented so far. Here we introduce machine
learning algorithms to the motion of artificial microswimmers with a hybrid
approach. We employ self-thermophoretic artificial microswimmers in a real
world environment [9, 10] which are controlled by a real-time microscopy system
to introduce reinforcement learning [11-13]. We demonstrate the solution of a
standard problem of reinforcement learning - the navigation in a grid world.
Due to the size of the microswimmer, noise introduced by Brownian motion if
found to contribute considerably to both the learning process and the actions
within a learned behavior. We extend the learning process to multiple swimmers
and sharing of information. Our work represents a first step towards the
integration of learning strategies into microsystems and provides a platform
for the study of the emergence of adaptive and collective behavior.
