Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/icaros-usc/pyribs
A bare-bones Python library for quality diversity optimization.
https://github.com/icaros-usc/pyribs
artificial-intelligence cma-es deep-learning evolutionary-algorithms evolutionary-computation map-elites openai-gym optimization python quality-diversity
Last synced: 3 months ago
JSON representation
A bare-bones Python library for quality diversity optimization.
- Host: GitHub
- URL: https://github.com/icaros-usc/pyribs
- Owner: icaros-usc
- License: mit
- Created: 2020-09-10T07:47:17.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2024-03-17T09:58:18.000Z (3 months ago)
- Last Synced: 2024-03-20T22:54:51.821Z (3 months ago)
- Topics: artificial-intelligence, cma-es, deep-learning, evolutionary-algorithms, evolutionary-computation, map-elites, openai-gym, optimization, python, quality-diversity
- Language: Python
- Homepage: https://pyribs.org
- Size: 48 MB
- Stars: 192
- Watchers: 14
- Forks: 29
- Open Issues: 20
-
Metadata Files:
- Readme: README.md
- Changelog: HISTORY.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Lists
- awesome-stars - pyribs - bones Python library for quality diversity optimization. | icaros-usc | 201 | (Python)
README
# pyribs
| Discord | Mailing List | Twitter |
| :-------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------: |
| [](https://discord.gg/QxhcJSqZ8G) | [Google Groups](https://groups.google.com/g/pyribs/) | [](https://twitter.com/pyribs) |
| Website | Source | Docs | Paper |
| :------------------------------: | :--------------------------------------------: | :----------------------------------------: | :------------------------------------------: |
| [pyribs.org](https://pyribs.org) | [GitHub](https://github.com/icaros-usc/pyribs) | [docs.pyribs.org](https://docs.pyribs.org) | [pyribs.org/paper](https://pyribs.org/paper) |
| PyPI | Conda | CI/CD | Docs Status |
| :---------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------: |
| [](https://pypi.python.org/pypi/ribs) | [](https://anaconda.org/conda-forge/pyribs) | [](https://github.com/icaros-usc/pyribs/actions?query=workflow%3A"Tests") | [](https://readthedocs.org/projects/ribs/) |
A _bare-bones_ Python library for quality diversity (QD) optimization. Pyribs
implements the highly modular _Rapid Illumination of Behavior Space (RIBS)_
framework for QD optimization. Pyribs is also the official implementation of
Covariance Matrix Adaptation MAP-Elites (CMA-ME), Covariance Matrix Adaptation
MAP-Elites via a Gradient Arborescence (CMA-MEGA), Covariance Matrix Adaptation
MAP-Annealing (CMA-MAE), and scalable variants of CMA-MAE.
## Community
**Join the
[Pyribs Announcements mailing list](https://groups.google.com/g/pyribs/)** for
infrequent updates (less than 1/month) on the status of the project and new
releases.
Need some help? Want to ask if pyribs is right for your project? Have a question
that is not quite a bug and not quite a feature request?
**Join the [community Discord](https://discord.gg/QxhcJSqZ8G)!**
## Overview
[Quality diversity (QD) optimization](https://arxiv.org/abs/2012.04322) 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
> "behavioral characteristics," "behavior descriptors," or "feature
> descriptors."
Recent years have seen the development of a large number of QD algorithms. To
represent these and future algorithms, we have developed the highly modular RIBS
framework. RIBS divides a QD algorithm into three components:
- An **archive**, which saves generated solutions within measure space.
- One or more **emitters**, where each emitter is an algorithm which generates
new candidate solutions and responds to feedback about how the solutions were
evaluated and how they were inserted into the archive.
- A **scheduler** that controls the interaction of the **archive** and
**emitters**. The **scheduler** also provides an interface for requesting new
candidate solutions and telling the algorithm how candidates performed.
By interchanging these components, a user can compose a large number of QD
algorithms.
Pyribs is an implementation of the RIBS framework designed to support a wide
range of users, from beginners entering the field to experienced researchers
seeking to develop new algorithms. Pyribs achieves these goals by embodying
three principles:
- **Simple:** Centered _only_ on components that are absolutely necessary to run
a QD algorithm, allowing users to combine the framework with other software
frameworks.
- **Flexible:** Capable of representing a wide range of current and future QD
algorithms, allowing users to easily create or modify components.
- **Accessible:** Easy to install and learn, particularly for beginners with
limited computational resources.
In contrast to other QD libraries, pyribs is "bare-bones." For example, like
[pycma](https://pypi.org/project/cma/), pyribs focuses solely on optimizing
fixed-dimensional continuous domains. Focusing 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](#additional-qd-libraries)
below if you need to handle additional use cases.
Following the RIBS framework (shown in the figure below), a standard algorithm
in pyribs operates as follows:
1. The user calls the `ask()` method on the scheduler. The scheduler requests
solutions from each emitter by calling the emitter's `ask()` method.
2. The user evaluates solutions to obtain the objective and measure values.
3. The user passes the evaluations to the scheduler's `tell()` method. The
scheduler adds the solutions into the archive and receives feedback. The
scheduler passes this feedback along with the evaluated solutions to each
emitter's `tell()` method, and each emitter then updates its internal state.
## Installation
pyribs supports Python 3.8 and above. The vast majority of users can install
pyribs by running:
```bash
# If you are on Mac, you may need to use quotations, e.g., pip install "ribs[visualize]"
pip install ribs[visualize]
```
The command above includes the `visualize` extra. If you will not be using the
pyribs visualization tools, you can install the base version of pyribs with:
```bash
pip install ribs
```
For more specific installation commands (e.g., installing from Conda or
installing from source), visit the
[installation selector](https://pyribs.org/#installation) on our website.
To test your installation, import pyribs and print the version with this
command:
```bash
python -c "import ribs; print(ribs.__version__)"
```
You should see a version number in the output.
## 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].
- Three instances of **EvolutionStrategyEmitter**, all of which start 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 emitters together.
After initializing the components, we optimize (pyribs maximizes) the negative
10-D Sphere function for 1000 iterations. Users of
[pycma](https://pypi.org/project/cma/) 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.
```python
import numpy as np
from ribs.archives import GridArchive
from ribs.emitters import EvolutionStrategyEmitter
from ribs.schedulers import Scheduler
archive = GridArchive(
solution_dim=10,
dims=[20, 20],
ranges=[(-1, 1), (-1, 1)],
)
emitters = [
EvolutionStrategyEmitter(
archive,
x0=[0.0] * 10,
sigma0=0.1,
) for _ in range(3)
]
scheduler = Scheduler(archive, emitters)
for itr in range(1000):
solutions = scheduler.ask()
# Optimize the 10D negative Sphere function.
objective_batch = -np.sum(np.square(solutions), axis=1)
# Measures: first 2 coordinates of each 10D solution.
measures_batch = solutions[:, :2]
scheduler.tell(objective_batch, measures_batch)
```
To visualize this archive with Matplotlib, we then use the
`grid_archive_heatmap` function from `ribs.visualize`.
```python
import matplotlib.pyplot as plt
from ribs.visualize import grid_archive_heatmap
grid_archive_heatmap(archive)
plt.show()
```
## Documentation
**The documentation is available online [here](https://docs.pyribs.org/). We
suggest that new users start with the
[tutorials](https://docs.pyribs.org/en/stable/tutorials.html).**
## Paper and Citation
Two years after the initial release of pyribs, we released a paper that
elaborates on the RIBS framework and the design decisions behind pyribs. For
more information on this paper, see [here](https://pyribs.org/paper). If you use
pyribs in your research, please consider citing this paper as follows. Also
consider citing any algorithms you use as shown
[below](#citing-algorithms-in-pyribs).
```
@inproceedings{10.1145/3583131.3590374,
author = {Tjanaka, Bryon and Fontaine, Matthew C and Lee, David H and Zhang, Yulun and Balam, Nivedit Reddy and Dennler, Nathaniel and Garlanka, Sujay S and Klapsis, Nikitas Dimitri and Nikolaidis, Stefanos},
title = {Pyribs: A Bare-Bones Python Library for Quality Diversity Optimization},
year = {2023},
isbn = {9798400701191},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3583131.3590374},
doi = {10.1145/3583131.3590374},
abstract = {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. Pyribs is available at https://pyribs.org},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
pages = {220–229},
numpages = {10},
keywords = {framework, quality diversity, software library},
location = {Lisbon, Portugal},
series = {GECCO '23}
}
```
## Contributors
pyribs is developed and maintained by the [ICAROS Lab](http://icaros.usc.edu) at
USC. For information on contributing to the repo, see
[CONTRIBUTING](./CONTRIBUTING.md).
- [Bryon Tjanaka](https://btjanaka.net)
- [Matthew C. Fontaine](https://scholar.google.com/citations?user=RqSvzikAAAAJ)
- [David H. Lee](https://github.com/itsdawei)
- [Yulun Zhang](https://github.com/lunjohnzhang)
- [Nivedit Reddy Balam](https://www.linkedin.com/in/nivedit-reddy)
- [Nathan Dennler](https://ndennler.github.io/)
- [Sujay S. Garlanka](https://sujaygarlanka.com)
- Nikitas Klapsis
- [Robby Costales](https://robbycostales.com)
- [Sam Sommerer](https://github.com/sam-sommerer)
- [Vincent Vu](https://vuvincent.com/)
- [Stefanos Nikolaidis](https://stefanosnikolaidis.net)
We thank [Amy K. Hoover](http://amykhoover.com/) and
[Julian Togelius](http://julian.togelius.com/) for their contributions deriving
the CMA-ME algorithm.
## Users
pyribs users include:
- [Adam Gaier (Autodesk Research)](https://scholar.google.com/citations?user=GGyARB8AAAAJ)
- [Adaptive & Intelligent Robotics Lab (Imperial College London)](https://www.imperial.ac.uk/adaptive-intelligent-robotics)
- [Chair of Statistical Learning and Data Science (LMU Munich)](https://www.slds.stat.uni-muenchen.de/)
- [Game Innovation Lab (New York University)](https://game.engineering.nyu.edu)
- [Giovanni Iacca (University of Trento)](https://sites.google.com/site/giovanniiacca/)
- [HUAWEI Noah's Ark Lab](https://github.com/huawei-noah)
- [ICAROS Lab (University of Southern California)](http://icaros.usc.edu/)
- [Jacob Schrum (Southwestern University)](https://github.com/schrum2/PyribsForGameGAN)
- [Lenia Research](https://lenia.world)
- [Paul Kent (The University of Warwick)](https://warwick.ac.uk/fac/sci/mathsys/people/students/2018intake/kent/)
- [Various](https://github.com/ganyariya/mario_pytorch)
[researchers](https://direct.mit.edu/isal/proceedings/isal/33/112256) at the
University of Tsukuba
### Publications
For the list of publications that use pyribs, refer to our
[Google Scholar entry](https://scholar.google.com/scholar?cites=1780392371068190395).
### Software
See the
[GitHub dependency graph](https://github.com/icaros-usc/pyribs/network/dependents)
for the public GitHub repositories which depend on pyribs.
## Citing Algorithms in pyribs
If you use the following algorithms, please consider citing their relevant
papers:
- **CMA-ME:** [Fontaine 2020](https://dl.acm.org/doi/10.1145/3377930.3390232)
```
@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}
}
```
- **CMA-MEGA:**
[Fontaine 2021](https://proceedings.neurips.cc/paper/2021/hash/532923f11ac97d3e7cb0130315b067dc-Abstract.html)
```
@inproceedings{NEURIPS2021_532923f1,
author = {Fontaine, Matthew and Nikolaidis, Stefanos},
booktitle = {Advances in Neural Information Processing Systems},
editor = {M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan},
pages = {10040--10052},
publisher = {Curran Associates, Inc.},
title = {Differentiable Quality Diversity},
url = {https://proceedings.neurips.cc/paper/2021/file/532923f11ac97d3e7cb0130315b067dc-Paper.pdf},
volume = {34},
year = {2021}
}
```
- **CMA-MAE:** [Fontaine 2022](https://arxiv.org/abs/2205.10752)
```
@misc{cmamae,
doi = {10.48550/ARXIV.2205.10752},
url = {https://arxiv.org/abs/2205.10752},
author = {Fontaine, Matthew C. and Nikolaidis, Stefanos},
keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Covariance Matrix Adaptation MAP-Annealing},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
- **Scalable CMA-MAE:** [Tjanaka 2022](https://arxiv.org/abs/2210.02622)
```
@ARTICLE{10243102,
author={Tjanaka, Bryon and Fontaine, Matthew C. and Lee, David H. and Kalkar, Aniruddha and Nikolaidis, Stefanos},
journal={IEEE Robotics and Automation Letters},
title={Training Diverse High-Dimensional Controllers by Scaling Covariance Matrix Adaptation MAP-Annealing},
year={2023},
volume={8},
number={10},
pages={6771-6778},
keywords={Covariance matrices;Training;Neural networks;Legged locomotion;Reinforcement learning;Evolutionary robotics;Evolutionary robotics;reinforcement learning},
doi={10.1109/LRA.2023.3313012}
}
```
## Additional QD Libraries
- [QDax](https://github.com/adaptive-intelligent-robotics/QDax): Implementations
of QD algorithms in JAX. QDax is suitable if you want to run entire QD
algorithms on hardware accelerators in a matter of minutes, and it is
particularly useful if you need to interface with Brax environments.
- [qdpy](https://gitlab.com/leo.cazenille/qdpy/): Python implementations of a
wide variety of QD algorithms.
- [sferes](https://github.com/sferes2/sferes2): Contains C++ implementations of
QD algorithms; can also handle discrete domains.
## License
pyribs is released under the
[MIT License](https://github.com/icaros-usc/pyribs/blob/master/LICENSE).
## Credits
The pyribs package was initially created with
[Cookiecutter](https://github.com/audreyr/cookiecutter) and the
[audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage)
project template.