pyribs

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pyribs.org	GitHub				docs.pyribs.org

A bare-bones Python library for quality diversity optimization. pyribs is the official implementation of the Covariance Matrix Adaptation MAP-Elites (CMA-ME) algorithm and implements the Rapid Illumination of Behavior Space (RIBS) redesign of MAP-Elites detailed in the paper Covariance Matrix Adapation for the Rapid Illumination of Behavior Space.

Overview

Quality diversity (QD) optimization is a subfield of optimization where solutions generated cover every point in a measure space while simultaneously maximizing (or minimizing) a single objective. QD algorithms within the MAP-Elites family of QD algorithms produce heatmaps (archives) as output where each cell contains the best discovered representative of a region in measure space.

In the QD literature, measure function outputs have also been referred to as "behavior characteristics," "behavior descriptors," or "feature descriptors."

While many QD libraries exist, this particular library aims to be the QD analog to the pycma library (a single objective optimization library). In contrast to other QD libraries, this library is "bare-bones," meaning pyribs (like pycma) focuses solely on optimizing fixed-dimensional continuous domains. Focusing solely on this one commonly-occurring problem allows us to optimize the library for performance as well as ease of use. Refer to the list of additional QD libraries if you need greater performance or have additional use cases.

A user of pyribs selects three components that meet the needs of their application:

• An Archive saves the best representatives generated within measure space.
• Emitters control how new candidate solutions are generated and affect if the algorithm prioritizes quality or diversity.
• A Scheduler joins the Archive and Emitters together and acts as a scheduling algorithm for emitters. The Scheduler provides an interface for requesting new candidate solutions and telling the algorithm how candidates performed.

Citation

If you use pyribs in your research, please cite it as follows. Note that you will need to include the hyperref package in order to use the \url command.

@misc{pyribs,
  title = {pyribs: A bare-bones Python library for quality diversity
           optimization},
  author = {Bryon Tjanaka and Matthew C. Fontaine and David H. Lee and
            Trung Tran Minh Vu and Yulun Zhang and Sam Sommerer and
            Nathan Dennler and Stefanos Nikolaidis},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/icaros-usc/pyribs}},
}

If you use the CMA-ME algorithm, please also cite Fontaine 2020.

@inproceedings{10.1145/3377930.3390232,
  author = {Fontaine, Matthew C. and Togelius, Julian and Nikolaidis, Stefanos and Hoover, Amy K.},
  title = {Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space},
  year = {2020},
  isbn = {9781450371285},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3377930.3390232},
  doi = {10.1145/3377930.3390232},
  booktitle = {Proceedings of the 2020 Genetic and Evolutionary Computation Conference},
  pages = {94–102},
  numpages = {9},
  location = {Canc\'{u}n, Mexico},
  series = {GECCO '20}
}

Usage

Here we show an example application of CMA-ME in pyribs. To initialize the algorithm, we first create:

• A 2D GridArchive where each dimension contains 20 cells across the range [-1, 1].
• An ImprovementEmitter, which starts from the search point 0 in 10 dimensional space and a Gaussian sampling distribution with standard deviation 0.1.
• A Scheduler that combines the archive and emitter together.

After initializing the components, we optimize (pyribs maximizes) the negative 10-D Sphere function for 1000 iterations. Users of pycma will be familiar with the ask-tell interface (which pyribs adopted). First, the user must ask the scheduler for new candidate solutions. After evaluating the solution, they tell the scheduler the objectives and measures of each candidate solution. The algorithm then populates the archive and makes decisions on where to sample solutions next. Our toy example uses the first two parameters of the search space as measures.

import numpy as np

from ribs.archives import GridArchive
from ribs.emitters import ImprovementEmitter
from ribs.scheduler import Scheduler

archive = GridArchive(solution_dim=len([0.0] * 10), dims=[20, 20], ranges=[(-1, 1), (-1, 1)])
emitters = [ImprovementEmitter(archive, [0.0] * 10, 0.1)]
scheduler = Scheduler(archive, emitters)

for itr in range(1000):
    solutions = scheduler.ask()

    objectives = -np.sum(np.square(solutions), axis=1)
    measures = solutions[:, :2]

    scheduler.tell(objectives, measures)

To visualize this archive with matplotlib, we then use the grid_archive_heatmap function from ribs.visualize.

import matplotlib.pyplot as plt
from ribs.visualize import grid_archive_heatmap

grid_archive_heatmap(archive)
plt.show()

For more information, refer to the documentation.

Installation

pyribs supports Python 3.7-3.10. Earlier Python versions may work but are not officially supported. To find the installation command for your system (including for installing from source), visit the installation selector on our website.

To test your installation, import pyribs and print the version with this command:

python -c "import ribs; print(ribs.__version__)"

You should see a version number in the output.

Documentation

See here for the documentation: https://docs.pyribs.org

To serve the documentation locally, clone the repo and install the development requirements with

pip install -e .[dev]

Then run

make servedocs

This will open a window in your browser with the documentation automatically loaded. Furthermore, every time you make changes to the documentation, the preview will also reload.

Contributors

pyribs is developed and maintained by the ICAROS Lab at USC.

• Bryon Tjanaka
• Matt Fontaine
• David Lee
• Yulun Zhang
• Vincent Vu
• Sam Sommerer
• Nathan Dennler
• Nikitas Klapsis
• Stefanos Nikolaidis

We thank Amy K. Hoover and Julian Togelius for their contributions deriving the CMA-ME algorithm.

Additional QD Libraries

• QDax: Implementations of QD algorithms in JAX -- suitable if you want to run entire QD algorithms on hardware accelerators in a matter of minutes, and particularly useful if you need to interface with Brax environments.
• qdpy: Python implementations of a wide variety of QD algorithms.
• sferes: Contains C++ implementations of QD algorithms; can also handle discrete domains.

Users

pyribs users include:

• Adam Gaier (Autodesk Research)
• Chair of Statistical Learning and Data Science (LMU Munich)
• Game Innovation Lab (New York University)
• ganyariya (University of Tsukuba)
• ICAROS Lab (University of Southern California)
• Jacob Schrum (Southwestern University)
• Lenia Research

Publications

For the list of publications which use pyribs, refer to our Google Scholar entry.

Software

See the GitHub dependency graph for the public GitHub repositories which depend on pyribs.

License

pyribs is released under the MIT License.

Credits

The pyribs package was initially created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.