  While the field of Quality-Diversity (QD) has grown into a distinct branch of
stochastic optimization, a few problems, in particular locomotion and
navigation tasks, have become de facto standards. Are such benchmarks
sufficient? Are they representative of the key challenges faced by QD
algorithms? Do they provide the ability to focus on one particular challenge by
properly disentangling it from others? Do they have much predictive power in
terms of scalability and generalization? Existing benchmarks are not
standardized, and there is currently no MNIST equivalent for QD. Inspired by
recent works on Reinforcement Learning benchmarks, we argue that the
identification of challenges faced by QD methods and the development of
targeted, challenging, scalable but affordable benchmarks is an important step.
As an initial effort, we identify three problems that are challenging in sparse
reward settings, and propose associated benchmarks: (1) Behavior metric bias,
which can result from the use of metrics that do not match the structure of the
behavior space. (2) Behavioral Plateaus, with varying characteristics, such
that escaping them would require adaptive QD algorithms and (3) Evolvability
Traps, where small variations in genotype result in large behavioral changes.
The environments that we propose satisfy the properties listed above.
