Assessing Quality-Diversity Neuro-Evolution Algorithms Performance in Hard Exploration Problems

Understand

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.