# pyribs: A Bare-Bones Python Library for Quality Diversity Optimization

arXiv: https://arxiv.org/abs/2303.00191 · HTML: https://mlsanity.com/paper/2303.00191/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 69 references that are in the map.
- First posted on arXiv 1 Mar 2023.

## Builds on (most cited first, 30 of 35)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [The CMA Evolution Strategy: A Tutorial](https://mlsanity.com/paper/1604.00772/) (2016)
- [Robots that can adapt like animals](https://mlsanity.com/paper/1407.3501/) (2014)
- [Quality and Diversity Optimization: A Unifying Modular Framework](https://mlsanity.com/paper/1708.09251/) (2017)
- [Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents](https://mlsanity.com/paper/1712.06560/) (2017)
- [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)
- [EGAD! an Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulation](https://mlsanity.com/paper/2003.01314/) (2020)
- [Quality-Diversity Optimization: a novel branch of stochastic optimization](https://mlsanity.com/paper/2012.04322/) (2020)
- [Autonomous skill discovery with Quality-Diversity and Unsupervised Descriptors](https://mlsanity.com/paper/1905.11874/) (2019)
- [Diversity Policy Gradient for Sample Efficient Quality-Diversity Optimization](https://mlsanity.com/paper/2006.08505/) (2020)
- [Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network](https://mlsanity.com/paper/2007.05674/) (2020)
- [Discovering the Elite Hypervolume by Leveraging Interspecies Correlation](https://mlsanity.com/paper/1804.03906/) (2018)
- [Deep Surrogate Assisted Generation of Environments](https://mlsanity.com/paper/2206.04199/) (2022)
- [Data-Efficient Design Exploration through Surrogate-Assisted Illumination](https://mlsanity.com/paper/1806.05865/) (2018)
- [Scaling MAP-Elites to Deep Neuroevolution](https://mlsanity.com/paper/2003.01825/) (2020)
- [Approximating Gradients for Differentiable Quality Diversity in Reinforcement Learning](https://mlsanity.com/paper/2202.03666/) (2022)
- [On the Importance of Environments in Human-Robot Coordination](https://mlsanity.com/paper/2106.10853/) (2021)
- [Illuminating Diverse Neural Cellular Automata for Level Generation](https://mlsanity.com/paper/2109.05489/) (2021)
- [Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding Boundaries](https://mlsanity.com/paper/1904.10656/) (2019)
- [Deep Surrogate Assisted MAP-Elites for Automated Hearthstone Deckbuilding](https://mlsanity.com/paper/2112.03534/) (2021)
- [Covariance Matrix Adaptation MAP-Annealing](https://mlsanity.com/paper/2205.10752/) (2022)
- [Quality Diversity for Multi-task Optimization](https://mlsanity.com/paper/2003.04407/) (2020)
- [A Quality Diversity Approach to Automatically Generating Human-Robot Interaction Scenarios in Shared Autonomy](https://mlsanity.com/paper/2012.04283/) (2020)
- [BOP-Elites, a Bayesian Optimisation algorithm for Quality-Diversity search](https://mlsanity.com/paper/2005.04320/) (2020)
- [Accelerated Quality-Diversity through Massive Parallelism](https://mlsanity.com/paper/2202.01258/) (2022)
- [Sparse Reward Exploration via Novelty Search and Emitters](https://mlsanity.com/paper/2102.03140/) (2021)

## Built on by (most cited first, 30 of 30)

- [GAVEL: Generating Games Via Evolution and Language Models](https://mlsanity.com/paper/2407.09388/) (2024)
- [Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning](https://mlsanity.com/paper/2305.13795/) (2023)
- [Multi-Robot Coordination and Layout Design for Automated Warehousing](https://mlsanity.com/paper/2305.06436/) (2023)
- [QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration](https://mlsanity.com/paper/2308.03665/) (2023)
- [Guidance Graph Optimization for Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2402.01446/) (2024)
- [CaDRE: Controllable and Diverse Generation of Safety-Critical Driving Scenarios using Real-World Trajectories](https://mlsanity.com/paper/2403.13208/) (2024)
- [The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning](https://mlsanity.com/paper/2405.03164/) (2024)
- [Arbitrarily Scalable Environment Generators via Neural Cellular Automata](https://mlsanity.com/paper/2310.18622/) (2023)
- [Quality-Diversity Red-Teaming: Automated Generation of High-Quality and Diverse Attackers for Large Language Models](https://mlsanity.com/paper/2506.07121/) (2025)
- [Surrogate Assisted Generation of Human-Robot Interaction Scenarios](https://mlsanity.com/paper/2304.13787/) (2023)
- [Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML](https://mlsanity.com/paper/2307.08364/) (2023)
- [Quality Diversity under Sparse Reward and Sparse Interaction: Application to Grasping in Robotics](https://mlsanity.com/paper/2308.05483/) (2023)
- [Multi-Agent Diagnostics for Robustness via Illuminated Diversity](https://mlsanity.com/paper/2401.13460/) (2024)
- [Diversifying Policy Behaviors with Extrinsic Behavioral Curiosity](https://mlsanity.com/paper/2410.06151/) (2024)
- [Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting Diversity](https://mlsanity.com/paper/2411.04466/) (2024)
- [AutoQD: Automatic Discovery of Diverse Behaviors with Quality-Diversity Optimization](https://mlsanity.com/paper/2506.05634/) (2025)
- [Destination-to-Chutes Task Mapping Optimization for Multi-Robot Coordination in Robotic Sorting Systems](https://mlsanity.com/paper/2510.03472/) (2025)
- [Density Descent for Diversity Optimization](https://mlsanity.com/paper/2312.11331/) (2023)
- [Algorithmic Scenario Generation as Quality Diversity Optimization](https://mlsanity.com/paper/2409.04711/) (2024)
- [Soft Quality-Diversity Optimization](https://mlsanity.com/paper/2512.00810/) (2025)
- [Discount Model Search for Quality Diversity Optimization in High-Dimensional Measure Spaces](https://mlsanity.com/paper/2601.01082/) (2026)
- [Optimization of Edge Directions and Weights for Mixed Guidance Graphs in Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2602.23468/) (2026)
- [Dynamic Quality-Diversity Search](https://mlsanity.com/paper/2404.05769/) (2024)
- [Towards Interpretable Foundation Models of Robot Behavior: A Task Specific Policy Generation Approach](https://mlsanity.com/paper/2407.08065/) (2024)
- [Online Behavior Modification for Expressive User Control of RL-Trained Robots](https://mlsanity.com/paper/2408.16776/) (2024)
- ["What are my options?": Explaining RL Agents with Diverse Near-Optimal Alternatives (Extended)](https://mlsanity.com/paper/2506.09901/) (2025)
- [Quality-Diversity Optimization as Multi-Objective Optimization](https://mlsanity.com/paper/2602.00478/) (2026)
- [Procedural Generation of First Person Shooter Maps using Map-Elites](https://mlsanity.com/paper/2605.30570/) (2026)
- [U-Net-Accelerated Quality-Diversity Optimization for Climate-Adaptive Urban Layouts](https://mlsanity.com/paper/2606.04658/) (2026)
- [Uncovering the Generative Landscape of Diffusion Models: A Spectral-Guided Quality-Diversity Diffusion Framework](https://mlsanity.com/paper/2610.15724/) (2026)

## Abstract (arXiv)

Recent years have seen a rise in the popularity of quality diversity (QD) optimization, a branch of optimization that seeks to find a collection of diverse, high-performing solutions to a given problem. To grow further, we believe the QD community faces two challenges: developing a framework to represent the field's growing array of algorithms, and implementing that framework in software that supports a range of researchers and practitioners. To address these challenges, we have developed pyribs, a library built on a highly modular conceptual QD framework. By replacing components in the conceptual framework, and hence in pyribs, users can compose algorithms from across the QD literature; equally important, they can identify unexplored algorithm variations. Furthermore, pyribs makes this framework simple, flexible, and accessible, with a user-friendly API supported by extensive documentation and tutorials. This paper overviews the creation of pyribs, focusing on the conceptual framework that it implements and the design principles that have guided the library's development.