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pyribs

A bare-bones Python library for quality diversity optimization.

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Efficient Illumination

pyribs is the official implementation of Covariance Matrix Adaptation MAP-Elites (CMA-ME) and other quality diversity optimization algorithms.

Flexible Components

Every quality diversity (QD) algorithm in pyribs consists of three components.

from ribs.archives import GridArchive

archive = GridArchive(
    solution_dim=10,
    dims=[20, 20],
    ranges=[(-1, 1), (-1, 1)],
)

The Archive is a data structure that stores solutions generated by the QD algorithm.

from ribs.emitters import EvolutionStrategyEmitter

emitters = [
    EvolutionStrategyEmitter(
        archive,
        x0=[0.0] * 10,
        sigma0=0.1,
    ) for _ in range(3)
]

Emitters generate new candidate solutions.

from ribs.schedulers import Scheduler

scheduler = Scheduler(archive, emitters)

The Scheduler facilitates the interaction of the archive and emitters and acts as a scheduling algorithm for emitters.

Intuitive Usage

pyribs components come together in an intuitive ask-tell interface inspired by pycma.

import numpy as np

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)

Insightful Visualizations

The pyribs visualize module integrates with Matplotlib to plot a heatmap of the archive after each experiment.

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

grid_archive_heatmap(archive)
plt.show()
Grid Archive Heatmap for the Sphere function

Easy Installation

pyribs supports Python 3.8 and above. The visualize distribution additionally installs visualization dependencies such as Matplotlib.

Distribution

Package

Command

Comprehensive Tutorials

Learn more about pyribs in the tutorials. All tutorials are also available on Google Colab.