  A fascinating aspect of nature lies in its ability to produce a collection of
organisms that are all high-performing in their niche. Quality-Diversity (QD)
methods are evolutionary algorithms inspired by this observation, that obtained
great results in many applications, from wing design to robot adaptation.
Recently, several works demonstrated that these methods could be applied to
perform neuro-evolution to solve control problems in large search spaces. In
such problems, diversity can be a target in itself. Diversity can also be a way
to enhance exploration in tasks exhibiting deceptive reward signals. While the
first aspect has been studied in depth in the QD community, the latter remains
scarcer in the literature. Exploration is at the heart of several domains
trying to solve control problems such as Reinforcement Learning and QD methods
are promising candidates to overcome the challenges associated. Therefore, we
believe that standardized benchmarks exhibiting control problems in high
dimension with exploration difficulties are of interest to the QD community. In
this paper, we highlight three candidate benchmarks and explain why they appear
relevant for systematic evaluation of QD algorithms. We also provide
open-source implementations in Jax allowing practitioners to run fast and
numerous experiments on few compute resources.
