Add tutorial page on Optuna integration (#492)

## Description

<!-- Provide a brief description of the PR's purpose here. -->

Created a short tutorial page explaining the Optuna integration.

## Status

- [x] I have read the guidelines in

[CONTRIBUTING.md](https://github.com/icaros-usc/pyribs/blob/master/CONTRIBUTING.md)
- [x] I have formatted my code using `yapf`
- [x] I have tested my code by running `pytest`
- [x] I have linted my code with `pylint`
- [x] I have added a one-line description of my change to the changelog
in
      `HISTORY.md`
- [x] This PR is ready to go

HISTORY.md

@@ -20,6 +20,7 @@
 
 #### Documentation
 
+- Add tutorial page on Optuna integration ({pr}`492`)
 - Switch from std to var in arm tutorial ({pr}`486`)
 - Fix documentation conf for readthedocs deprecations ({pr}`485`)
 - Add novelty search with CMA-ES to sphere example ({pr}`478`, {pr}`482`)

docs/tutorials.md

@@ -41,17 +41,19 @@ the other algorithm tutorials.
 tutorials/lsi_mnist
 tutorials/arm_repertoire
 tutorials/fooling_mnist
+tutorials/optuna
 ```
 
-The following tutorials show how pyribs can implement a variety of algorithms
-for different problems.
+The following tutorials show how pyribs can be applied to a variety of problems.
 
 - {doc}`tutorials/lsi_mnist`: Generates diverse handwritten MNIST digits with
   CMA-ME.
 - {doc}`tutorials/arm_repertoire`: Combines CMA-ME with a CVTArchive to search
   for optimal configurations for a robot arm.
 - {doc}`tutorials/fooling_mnist`: Searches for misclassified MNIST images with
   MAP-Elites.
+- {doc}`tutorials/optuna`: Details on how pyribs can be integrated with the
+  [Optuna](https://optuna.org) framework for hyperparameter optimization.
 
 <!--
 

tutorials/optuna.md

@@ -0,0 +1,15 @@
+# Integrating with Optuna
+
+[Optuna](https://optuna.org/) is an open source hyperparameter optimization
+framework. Currently, there is limited support for using pyribs in Optuna via
+plugins that are available on [OptunaHub](https://hub.optuna.org):
+
+- [CMA-MAE Sampler](https://hub.optuna.org/samplers/cmamae/) provides an Optuna
+  sampler that uses CMA-MAE as implemented in pyribs. This enables one to search
+  for diverse, high-performing hyperparameters using CMA-MAE.
+- [Pyribs Visualization Wrappers](https://hub.optuna.org/visualization/plot_pyribs/)
+  provides wrappers around pyribs visualization tools, enabling visualizing
+  results from the CMA-MAE sampler.
+
+For more information, please refer to the documentation for these plugins and to
+the more general Optuna documentation.