# QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration

arXiv: https://arxiv.org/abs/2308.03665 · HTML: https://mlsanity.com/paper/2308.03665/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 35 references that are in the map.
- First posted on arXiv 7 Aug 2023.

## Builds on (most cited first, 26 of 26)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Diversity is All You Need: Learning Skills without a Reward Function](https://mlsanity.com/paper/1802.06070/) (2018)
- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation](https://mlsanity.com/paper/2106.13281/) (2021)
- [Population Based Training of Neural Networks](https://mlsanity.com/paper/1711.09846/) (2017)
- [Dynamics-Aware Unsupervised Discovery of Skills](https://mlsanity.com/paper/1907.01657/) (2019)
- [Robots that can adapt like animals](https://mlsanity.com/paper/1407.3501/) (2014)
- [One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL](https://mlsanity.com/paper/2010.14484/) (2020)
- [Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space](https://mlsanity.com/paper/1912.02400/) (2019)
- [Differentiable Quality Diversity](https://mlsanity.com/paper/2106.03894/) (2021)
- [Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional Archive of Phenotypic Elites Algorithm](https://mlsanity.com/paper/1610.05729/) (2016)
- [Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX](https://mlsanity.com/paper/2306.09884/) (2023)
- [Diversity Policy Gradient for Sample Efficient Quality-Diversity Optimization](https://mlsanity.com/paper/2006.08505/) (2020)
- [Scaling MAP-Elites to Deep Neuroevolution](https://mlsanity.com/paper/2003.01825/) (2020)
- [pyribs: A Bare-Bones Python Library for Quality Diversity Optimization](https://mlsanity.com/paper/2303.00191/) (2023)
- [EvoJAX: Hardware-Accelerated Neuroevolution](https://mlsanity.com/paper/2202.05008/) (2022)
- [Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery](https://mlsanity.com/paper/2210.03516/) (2022)
- [Multi-Objective Quality Diversity Optimization](https://mlsanity.com/paper/2202.03057/) (2022)
- [Empowering Quality Diversity in Dungeon Design with Interactive Constrained MAP-Elites](https://mlsanity.com/paper/1906.05175/) (2019)
- [Empirical analysis of PGA-MAP-Elites for Neuroevolution in Uncertain Domains](https://mlsanity.com/paper/2210.13156/) (2022)
- [Accelerated Quality-Diversity through Massive Parallelism](https://mlsanity.com/paper/2202.01258/) (2022)
- [Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning](https://mlsanity.com/paper/2211.02193/) (2022)
- [Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs through Surrogate-Assisted Illumination](https://mlsanity.com/paper/1702.03713/) (2017)
- [Evolving Populations of Diverse RL Agents with MAP-Elites](https://mlsanity.com/paper/2303.12803/) (2023)
- [Assessing Quality-Diversity Neuro-Evolution Algorithms Performance in Hard Exploration Problems](https://mlsanity.com/paper/2211.13742/) (2022)

## Built on by (most cited first, 17 of 17)

- [CAX: Cellular Automata Accelerated in JAX](https://mlsanity.com/paper/2410.02651/) (2024)
- [Dominated Novelty Search: Rethinking Local Competition in Quality-Diversity](https://mlsanity.com/paper/2502.00593/) (2025)
- [Synergizing Quality-Diversity with Descriptor-Conditioned Reinforcement Learning](https://mlsanity.com/paper/2401.08632/) (2023)
- [Diversity from Human Feedback](https://mlsanity.com/paper/2310.06648/) (2023)
- [Mix-ME: Quality-Diversity for Multi-Agent Learning](https://mlsanity.com/paper/2311.01829/) (2023)
- [Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization](https://mlsanity.com/paper/2502.02190/) (2025)
- [Multi-Objective Quality-Diversity in Unstructured and Unbounded Spaces](https://mlsanity.com/paper/2504.03715/) (2025)
- [Breaking the Performance Ceiling in Reinforcement Learning requires Inference Strategies](https://mlsanity.com/paper/2505.21236/) (2025)
- [When Does Neuroevolution Outcompete Reinforcement Learning in Transfer Learning Tasks?](https://mlsanity.com/paper/2505.22696/) (2025)
- [Quality-Diversity with Limited Resources](https://mlsanity.com/paper/2406.03731/) (2024)
- [Preference-Conditioned Gradient Variations for Multi-Objective Quality-Diversity](https://mlsanity.com/paper/2411.12433/) (2024)
- [EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning](https://mlsanity.com/paper/2501.15129/) (2025)
- [Crazyflow: An Accurate, GPU-Accelerated, Differentiable Drone Simulator in JAX](https://mlsanity.com/paper/2606.01478/) (2026)
- [Scaling Policy Gradient Quality-Diversity with Massive Parallelization via Behavioral Variations](https://mlsanity.com/paper/2501.18723/) (2025)
- [Distributional Value Estimation Without Target Networks for Robust Quality-Diversity](https://mlsanity.com/paper/2604.20381/) (2026)
- [Contextual Quality-Diversity Evolutionary Reinforcement Learning for HVAC Control in Tropical Commercial Buildings](https://mlsanity.com/paper/2608.11324/) (2026)
- [Behavioral Foundation Models for Quality Diversity](https://mlsanity.com/paper/2609.35615/) (2026)

## Abstract (arXiv)

QDax is an open-source library with a streamlined and modular API for Quality-Diversity (QD) optimization algorithms in Jax. The library serves as a versatile tool for optimization purposes, ranging from black-box optimization to continuous control. QDax offers implementations of popular QD, Neuroevolution, and Reinforcement Learning (RL) algorithms, supported by various examples. All the implementations can be just-in-time compiled with Jax, facilitating efficient execution across multiple accelerators, including GPUs and TPUs. These implementations effectively demonstrate the framework's flexibility and user-friendliness, easing experimentation for research purposes. Furthermore, the library is thoroughly documented and tested with 95% coverage.