AbstractWe employQlearning, a variant of reinforcement learning, so that an active particle learns by itself to navigate on the fastest path toward a target while experiencing external forces and flow fields. As state variables, we use the distance and direction toward the target, and as action variables the active particle can choose a new orientation along which it moves with constant velocity. We explicitly investigate optimal navigation in a potential barrier/well and a uniform/ Poiseuille/swirling flow field. We show thatQlearning is able to identify the fastest path and discuss the results. We also demonstrate thatQlearning and applying the learned policy works when the particle orientation experiences thermal noise. However, the successful outcome strongly depends on the specific problem and the strength of noise.Graphical abstract