  Quality-Diversity algorithms search for large collections of diverse and
high-performing solutions, rather than just for a single solution like typical
optimisation methods. They are specially adapted for multi-modal problems that
can be solved in many different ways, such as complex reinforcement learning or
robotics tasks. However, these approaches are highly dependent on the choice of
feature descriptors (FDs) quantifying the similarity in behaviour of the
solutions. While FDs usually needs to be hand-designed, recent studies have
proposed ways to define them automatically by using feature extraction
techniques, such as PCA or Auto-Encoders, to learn a representation of the
problem from previously explored solutions. Here, we extend these approaches to
more complex problems which cannot be efficiently explored by relying only on a
single representation but require instead a set of diverse and complementary
representations. We describe MC-AURORA, a Quality-Diversity approach that
optimises simultaneously several collections of solutions, each with a
different set of FDs, which are, in turn, defined automatically by an ensemble
of modular auto-encoders. We show that this approach produces solutions that
are more diverse than those produced by single-representation approaches.
