Module 18: Quality-Diversity Algorithms - Not Just Good Solutions - Diverse Good Solutions

QD algorithms fill an archive of diverse, high-performing solutions. A robot with a repertoire of gaits. A design tool showing many Pareto-optimal shapes. MAP-Elites illuminates the solution space in ways single-objective search never could.

Learning Objectives

1. Understand the QD paradigm (quality + behavioral diversity)
2. Implement MAP-Elites from scratch
3. Understand novelty search and curiosity-driven exploration
4. Know CMA-ME and differentiable QD
5. Apply QD to a real domain (robot gaits, design, game levels)

Concept Explanation

Coming soon.

Code Examples

Coming soon.

Exercises

Coming soon.

Milestone Checklist

• Built MAP-Elites from scratch
• Visualized a filled behavior-performance archive
• Understand novelty search
• Know when QD is better than single-objective