<ul><li>Vector Quantized-Elites is a novel Quality-Diversity algorithm that constructs a structured behavioral space grid.</li><li>It eliminates the need for prior task-specific knowledge by using unsupervised learning.</li><li>The integration of Vector Quantized Variational Autoencoders enables dynamic learning of behavioral descriptors.</li><li>VQ-Elites demonstrates its ability to efficiently generate diverse, high-quality solutions in robotic arm and mobile robot tasks.</li></ul>

Vector Quantized-Elites: Unsupervised and Problem-Agnostic Quality-Diversity Optimization

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