  During the last two decades, various models have been proposed for fish
collective motion. These models are mainly developed to decipher the biological
mechanisms of social interaction between animals. They consider very simple
homogeneous unbounded environments and it is not clear that they can simulate
accurately the collective trajectories. Moreover when the models are more
accurate, the question of their scalability to either larger groups or more
elaborate environments remains open. This study deals with learning how to
simulate realistic collective motion of collective of zebrafish, using
real-world tracking data. The objective is to devise an agent-based model that
can be implemented on an artificial robotic fish that can blend into a
collective of real fish. We present a novel approach that uses Quality
Diversity algorithms, a class of algorithms that emphasise exploration over
pure optimisation. In particular, we use CVT-MAP-Elites, a variant of the
state-of-the-art MAP-Elites algorithm for high dimensional search space.
Results show that Quality Diversity algorithms not only outperform classic
evolutionary reinforcement learning methods at the macroscopic level (i.e.
group behaviour), but are also able to generate more realistic biomimetic
behaviours at the microscopic level (i.e. individual behaviour).
