Quantitative understanding of behaviour of living organisms is a key topic in biophysical and active matter literature. In particular, interest has arisen around interacting micro-swimmers, which is an umbrella term for small aquatic organisms, from bacteria to small animals. Recent advances in reinforcement learning, a form of machine learning, allow for more than descriptive analysis and open the possibility of probing how certain behaviour can emerge as optimal strategies of self-interested agents. In this work, we focus on prey-predator interactions in low-Reynolds hydrodynamics. We assume agents can only receive information via hydrodynamical sensing and play a zero-sum game: while the predator purpose is to catch the prey, the prey has to avoid encounters. Since both agents have to rely on incomplete information, trivial strategies are not available in principle. We apply a machine learning algorithm in order to be able to get insight both in the strategies and how hydrodynamical information is relevant.