add CONTRIBUTING.md and add more info to front page of documentation.

CONTRIBUTING.md

@@ -0,0 +1,36 @@
+# Contributing
+
+Contributions and suggestions are welcome!
+If you are interested in contributing either bug fixes or new features, open an issue and we can talk about it!
+New PRs will require:
+
+1. New unit tests for any new or changed common module, and all unit tests should pass.
+2. All code should follow a similar style to the rest of the repository and the linter should pass.
+3. Documentation of new features.
+4. Manual approval.
+
+
+We use the [GitFlow](https://datasift.github.io/gitflow/IntroducingGitFlow.html) model, meaning that all PRs should be opened against the `develop` branch!
+To begin, you can run the following commands:
+
+```
+git clone https://github.com/cpnota/autonomous-learning-library.git
+cd autonomous-learning-library
+git checkout develop
+pip install -e .[docs]
+```
+
+The unit tests may be run using:
+
+```
+make test
+```
+
+Finally, you rebuild the documentation using:
+
+```
+cd docs
+make clean && make html
+```
+
+Happy hacking!

README.md

@@ -58,7 +58,13 @@ Alternately, you can install directly from this repository:
 git clone https://github.com/cpnota/autonomous-learning-library.git
 cd autonomous-learning-library
 pip install -e .
-``` 
+```
+
+You can also install the prerequisites using:
+
+```
+pip install autonomous-learning-library[pytorch]
+```
 
 ## Running the Presets
 
@@ -84,24 +90,6 @@ python scripts/watch_atari.py Breakout "runs/_a2c [id]"
 where `id` is the ID of your particular run. You should should be able to find it using tab completion or by looking in the `runs` directory.
 The `autonomous-learning-library` also contains presets and scripts for classic control and PyBullet environments.
 
-## Contributing
-
-Contributions and suggestions are welcome!
-However, require a high-level of code quality, including automated unit tests for common modules and linting.
-The unit tests may be run using:
-
-```
-make test
-```
-
-The linter (pylint) can be run using:
-
-```
-make lint
-```
-
-
-
 ## Note
 
 This library was built in the [Autonomous Learning Laboratory](http://all.cs.umass.edu) (ALL) at the [University of Massachusetts, Amherst](https://www.umass.edu).

docs/source/guide/benchmark_performance.rst

@@ -5,6 +5,7 @@ Reinforcement learning algorithms are difficult to debug and test.
 For this reason, in order to ensuring the correctness of the preset agents provided by the ``autonomous-learning-library``,
 we benchmark each algorithm after every major change.
 We also discuss the performance of our implementations relative to published results.
+For our hyperparameters for each domain, see :ref:`all.presets`.
 
 Atari Benchmark
 ---------------

docs/source/index.rst

@@ -1,8 +1,20 @@
 The Autonomous Learning Library
 ===============================
 
-The Autonomous Learning Library is a toolkit for building and evaluating reinforcement learning agents.
-This documentation is under construction!
+The `Autonomous Learning Library <https://github.com/cpnota/autonomous-learning-library>`_ is a PyTorch-based toolkit for building and evaluating reinforcement learning agents.
+
+.. image:: environments.png
+    :align: center
+
+Here are some common links:
+
+* The `GitHub <https://github.com/cpnota/autonomous-learning-library>`_ repository.
+* The :ref:`Getting Started` guide.
+* An `example project <https://github.com/cpnota/all-example-project>`_ to help you get started building your own agents.
+* The :ref:`Benchmark Performance` for our preset agents.
+* The :ref:`all.presets` documentation, including default hyperparameters.
+
+Enjoy!
 
 .. toctree::
     :maxdepth: 2