Quality-Diversity & Novelty Search Reading List

Curated by Mouhssine Rifaki | Stanford Electrical Engineering | Last updated August 2026

Foundational and modern papers on quality-diversity optimization, novelty search, and illumination algorithms such as MAP-Elites.

Quality-Diversity & Novelty Search: 10 key papers

1. Illuminating search spaces by mapping elites
   Mouret and Clune. arXiv 2015.
2. Quality Diversity: A New Frontier for Evolutionary Computation
   Pugh et al. Frontiers in Robotics and AI 2016.
3. Robots that can adapt like animals
   Cully et al. arXiv 2014.
4. Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space
   Fontaine et al. arXiv 2019.
5. Differentiable Quality Diversity
   Fontaine and Nikolaidis. arXiv 2021.
6. Approximating Gradients for Differentiable Quality Diversity in Reinforcement Learning
   Tjanaka et al. arXiv 2022.
7. Policy gradient assisted MAP-Elites
   Nilsson and Cully. 2021.
8. Scaling MAP-Elites to Deep Neuroevolution
   Colas et al. arXiv 2020.
9. Multi-emitter MAP-elites
   Cully. 2021.
10. Discovering Diverse Nearly Optimal Policies with Successor Features
    Zahavy et al. arXiv 2021.

Related reading lists

• Zero-Shot RL & Generalization
• Neural Architecture Search
• Optimal Transport for Machine Learning
• Classical Planning & Search Algorithms
• Curiosity-Driven Exploration
• Monte Carlo Tree Search
• Meta-Reinforcement Learning
• State Space Models & Structured SSMs