A prevalent limitation of optimizing over a single objective is that it can
be misguided, becoming trapped in local optimum. This can be rectified by
Quality-Diversity (QD) algorithms, where a population of high-quality and
diverse solutions to a problem is preferred. Most conventional QD approaches,
for example, MAP-Elites, explicitly manage a behavioral archive where solutions
are broken down into predefined niches. In this work, we show that a diverse
population of solutions can be found without the limitation of needing an
archive or defining the range of behaviors in advance. Instead, we break down
solutions into independently evolving species and use unsupervised skill
discovery to learn diverse, high-performing solutions. We show that this can be
done through gradient-based mutations that take on an information theoretic
perspective of jointly maximizing mutual information and performance. We
propose Diverse Quality Species (DQS) as an alternative to archive-based QD
algorithms. We evaluate it over several simulated robotic environments and show
that it can learn a diverse set of solutions from varying species. Furthermore,
our results show that DQS is more sample-efficient and performant when compared
to other QD algorithms. Relevant code and hyper-parameters are available at:
this https URL

本文提出了一种名为 Diverse Quality Species (DQS) 的新型质量 - 多样性算法，能够在不需要存档或事先定义行为范围的情况下，将解决方案分解为独立进化种类，并利用无监督技能发现来学习多样化而高性能的解决方案，在多个仿真机器人环境中进行评估，结果表明 DQS 比其他 QD 算法更具样本效率和性能。