We recommend new users start with these tutorials which demonstrate how to use the key algorithms in pyribs.
Using CMA-ME to Land a Lunar Lander Like a Space Shuttle: Covers the CMA-ME algorithm and various basic library features.
Upgrading CMA-ME to CMA-MAE on the Sphere Benchmark: Shows how to implement CMA-MAE, a powerful algorithm built on CMA-ME, on the sphere linear projection benchmark.
Generating Tom Cruise Images with DQD Algorithms: Covers CMA-MEGA and CMA-MAEGA, two algorithms designed for differentiable quality diversity problems (QD problems where gradients are available).
The following tutorials show how pyribs can implement a variety of algorithms for different problems.
Illuminating the Latent Space of an MNIST GAN: Generates diverse handwritten MNIST digits with CMA-ME.
Learning a Repertoire of Robot Arm Configurations: Combines CMA-ME with a CVTArchive to search for optimal configurations for a robot arm.
Generating Images to Fool an MNIST Classifier: Searches for misclassified MNIST images with MAP-Elites.
Finally, these tutorials provide a closer look at some of the features of pyribs.
Example Feature: Placeholder for upcoming tutorials!
If you would like to run the tutorials locally, follow these instructions:
Download the notebooks from GitHub here.
Install Jupyter Lab:
pip install jupyterlab
Start Jupyter Lab. This should open a window in your browser.
Open the notebook from within the Jupyter Lab browser window.
Note that each notebook contains cell magic that installs the dependencies it needs for execution, so even if you have not installed the dependencies on your own, running the notebook will install the dependencies for you.