  We consider a model of two competing microswimming agents engaged in a
pursue-evasion task within a low-Reynolds-number environment. Agents can only
perform simple maneuvers and sense hydrodynamic disturbances, which provide
ambiguous (partial) information about the opponent's position and motion. We
frame the problem as a zero-sum game: The pursuer has to capture the evader in
the shortest time, while the evader aims at deferring capture as long as
possible. We show that the agents, trained via adversarial reinforcement
learning, are able to overcome partial observability by discovering
increasingly complex sequences of moves and countermoves that outperform known
heuristic strategies and exploit the hydrodynamic environment.
