QDax
A hardware-accelerated Python library for running Quality-Diversity and neuroevolution algorithms in minutes instead of days.
What is QDax?
QDax is a Python library that accelerates Quality-Diversity and neuroevolution algorithms using hardware accelerators like GPUs and TPUs. It solves the problem of extremely long runtimes for these population-based optimization methods, enabling researchers to run experiments in minutes instead of days or weeks. The framework is built on JAX for parallelization and is designed to be flexible and extensible for various research problems.
Researchers and practitioners in machine learning, robotics, and evolutionary computation who work with Quality-Diversity optimization, neuroevolution, or need to run large-scale population-based algorithms efficiently.
Developers choose QDax for its unparalleled speedup of QD algorithms through hardware acceleration, its comprehensive implementation of state-of-the-art algorithms, and its design as an extensible research framework that facilitates rapid prototyping and experimentation.
Overview
Accelerated Quality-Diversity
Use Cases
Best For
- Accelerating MAP-Elites and other QD algorithm research on GPU/TPU hardware
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