fix rst issues

automl · May 30, 2024 · 1ac6b90 · 1ac6b90
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diff --git a/docs/CONTRIBUTING.md b/docs/CONTRIBUTING.md
@@ -0,0 +1,121 @@
+# Contributing
+
+Contributions are welcome, and they are greatly appreciated! Every little bit
+helps, and credit will always be given.
+
+You can contribute in many ways:
+
+## Types of Contributions
+
+### Report Bugs
+
+Report bugs at https://github.com/automl/arlbench/issues.
+
+If you are reporting a bug, please include:
+
+- Your operating system name and version.
+- Any details about your local setup that might be helpful in troubleshooting.
+- Detailed steps to reproduce the bug.
+
+### Fix Bugs
+
+Look through the GitHub issues for bugs. Anything tagged with "bug" and "help
+wanted" is open to whoever wants to implement it.
+
+### Implement Features
+
+Look through the GitHub issues for features. Anything tagged with "enhancement"
+and "help wanted" is open to whoever wants to implement it.
+
+### Write Documentation
+
+ARLBench could always use more documentation, whether as part of the
+official ARLBench docs, in docstrings, or even on the web in blog posts,
+articles, and such.
+
+### Submit Feedback
+
+The best way to send feedback is to file an issue at https://github.com/automl/arlbench/issues.
+
+If you are proposing a feature:
+
+- Explain in detail how it would work.
+- Keep the scope as narrow as possible, to make it easier to implement.
+- Remember that this is a volunteer-driven project, and that contributions are welcome :)
+
+## Get Started!
+
+Ready to contribute? Here's how to set up `arlbench` for local development.
+
+1. Fork the `arlbench` repo on GitHub.
+2. Clone your fork locally:
+```
+    $ git clone git@github.com:your_name_here/arlbench.git
+```
+
+3. Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:
+```
+    $ mkvirtualenv arlbench
+    $ cd arlbench/
+    $ python setup.py develop
+```
+
+4. Create a branch for local development:
+```
+    $ git checkout -b name-of-your-bugfix-or-feature
+```
+
+   Now you can make your changes locally.
+
+5. When you're done making changes, check that your changes pass ruff, including testing other Python versions with tox:
+```
+    $ ruff format arlbench tests
+    $ python setup.py test or pytest
+    $ tox
+```
+
+   To get flake8 and tox, just pip install them into your virtualenv.
+
+6. Commit your changes and push your branch to GitHub:
+```
+    $ git add .
+    $ git commit -m "Your detailed description of your changes."
+    $ git push origin name-of-your-bugfix-or-feature
+```
+
+7. Submit a pull request through the GitHub website.
+
+## Pull Request Guidelines
+
+Before you submit a pull request, check that it meets these guidelines:
+
+1. The pull request should include tests.
+2. If the pull request adds functionality, the docs should be updated. Put
+   your new functionality into a function with a docstring, and add the
+   feature to the list in README.rst.
+3. The pull request should work for Python 3.5, 3.6, 3.7 and 3.8, and for PyPy. Check
+   https://travis-ci.com/automl/arlbench/pull_requests
+   and make sure that the tests pass for all supported Python versions.
+
+## Tips
+
+To run a subset of tests:
+
+```
+$ pytest tests.test_arlbench
+
+```
+
+## Deploying
+
+A reminder for the maintainers on how to deploy.
+Make sure all your changes are committed (including an entry in HISTORY.rst).
+Then run:
+
+```
+$ bump2version patch # possible: major / minor / patch
+$ git push
+$ git push --tags
+```
+
+Travis will then deploy to PyPI if tests pass.
diff --git a/docs/advanced_usage/index.rst b/docs/advanced_usage/index.rst
@@ -3,6 +3,10 @@ Advanced Configuration Options
 
 .. toctree::
    :maxdepth: 2
+
+   dynamic_configuration
+   algorithm_states
+   autorl_paradigms
 
 
 .. warning::

diff --git a/docs/basic_usage/env_subsets.rst b/docs/basic_usage/env_subsets.rst
@@ -3,7 +3,7 @@ The ARLBench Subsets
 
 We analyzed the hyperparameter landscapes of PPO, DQN and SAC on 20 environments to select a subset which allows for efficient benchmarking of AutoRL algorithms. These are the resulting subsets:
 
-.. image:: path/subsets.png
+.. image:: ../images/subsets.png
   :width: 800
   :alt: Environment subsets for PPO, DQN and SAC
 

diff --git a/docs/basic_usage/index.rst b/docs/basic_usage/index.rst
@@ -11,8 +11,14 @@ The most important question, however, is how to actually use ARLBench in your ex
 4. Use the pre-defined search spaces to run your AutoRL method for several runs. If there is a good reason to deviate from these search spaces, please report this alongside your results.
 5. Evaluate the best found configuration on the environment test seeds and report this result.
 
+
+In-depth Information on:
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
 .. toctree::
    :maxdepth: 2
+
    objectives
    env_subsets
-   seeding
+   seeding
+   options
diff --git a/docs/basic_usage/options.rst b/docs/basic_usage/options.rst
@@ -1,5 +1,5 @@
-ARLBench Options
-================
+Options for ARLBench 
+====================
 
 A given training run in ARLBench can be configured on two levels: the lower one is the configuration that happens via the AutoRL tool we benchmark while the upper level decides the setting we test the AutoRL tool in.
 The high level configuration takes place via the 'autorl' keys in the configuration file. These are the available options:

diff --git a/docs/conf.py b/docs/conf.py
@@ -7,7 +7,7 @@
     "copyright": __copyright__,
     "author": __author__,
     "version": __version__,
-    "name": __name__,
+    "name": "ARLBench",
     "html_theme_options": {
         "github_url": "https://github.com/automl/ARLBench",
         "twitter_url": "https://twitter.com/automl_org?lang=de",

diff --git a/docs/contributing.rst b/docs/contributing.rst
diff --git a/docs/index.rst b/docs/index.rst
@@ -1,5 +1,5 @@
-Home
-====
+ARLBench
+========
 
 .. toctree::
    :hidden:
@@ -12,17 +12,20 @@ Home
    api
    glossary
    faq
-   contributing
+   :caption: Contributing to ARLBench:
+
+   CONTRIBUTING.md
 
 
 Welcome to ARLBench, your pathway into efficient AutoRL!
 We offer lightning-fast implementations of PPO, DQN and SAC that are highly configurable as well as a subset of environments which will give you a representative result of your AutoRL method across the RL landscape.
 This documentation, in combination with our examples, should provide you with a starting point to get your AutoRL method up and running in no time.
 
-.. image:: path/structure.png
+.. image:: images/structure.png
   :width: 800
   :alt: ARLBench overview figure
 
+
 You will interact with ARLBench through the `AutoRL Environment` class, which is the main entry point for all the functionalities that ARLBench offers.
 It works similarly to a gymnasium environment: you can use 'reset' to start a fresh run and then 'step' to take one configuration step. 
 Between steps, you can change your configuration to allow for flexible schedules.

diff --git a/docs/modules.rst b/docs/modules.rst
@@ -0,0 +1,7 @@
+arlbench
+========
+
+.. toctree::
+   :maxdepth: 4
+
+   arlbench