  We consider the problem of two active particles in 2D complex flows with the
multi-objective goals of minimizing both the dispersion rate and the energy
consumption of the pair. We approach the problem by means of Multi Objective
Reinforcement Learning (MORL), combining scalarization techniques together with
a Q-learning algorithm, for Lagrangian drifters that have variable swimming
velocity. We show that MORL is able to find a set of trade-off solutions
forming an optimal Pareto frontier. As a benchmark, we show that a set of
heuristic strategies are dominated by the MORL solutions. We consider the
situation in which the agents cannot update their control variables
continuously, but only after a discrete (decision) time, $\tau$. We show that
there is a range of decision times, in between the Lyapunov time and the
continuous updating limit, where Reinforcement Learning finds strategies that
significantly improve over heuristics. In particular, we discuss how large
decision times require enhanced knowledge of the flow, whereas for smaller
$\tau$ all a priori heuristic strategies become Pareto optimal.
