  Finding the best strategy to minimize the time needed to find a given target
is a crucial task both in nature and in reaching decisive technological
advances. By considering learning agents able to switch their dynamics between
standard and active Brownian motion, here we focus on developing effective
target-search behavioral policies for microswimmers navigating a homogeneous
environment and searching for targets of unknown position. We exploit
Projective Simulation, a reinforcement learning algorithm, to acquire an
efficient stochastic policy represented by the probability of switching the
phase, i.e. the navigation mode, in response to the type and the duration of
the current phase. Our findings reveal that the target-search efficiency
increases with the particle's self-propulsion during the active phase and that,
while the optimal duration of the passive case decreases monotonically with the
activity, the optimal duration of the active phase displays a non-monotonic
behavior.
