Quality diversity

Evolution, Reinforcement learning, Search
(Pugh, Soros, and Stanley 2016; Cully and Demiris 2017)

QD is about creating algorithms that favor diversity in searching the space. In QD, one needs to both:

  • Measure the quality of a solution
  • Have a way to describe the effect of a solution

Solutions in QD have to be good in the two above ways.

QD is also a form of novelty search.


Cully, Antoine, and Yiannis Demiris. 2017. “Quality and Diversity Optimization: A Unifying Modular Framework.” IEEE Transactions on Evolutionary Computation 22 (2). IEEE:245–59.

Pugh, Justin K., Lisa B. Soros, and Kenneth O. Stanley. 2016. “Quality Diversity: A New Frontier for Evolutionary Computation.” Frontiers in Robotics and AI 3. Frontiers.

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