Feature/mla 2394 update web docs (#37)

* Added mkdocs and refactored docs.

* Updated docs.

* Added gh action to manually public docs. Updated mkdocs.yml.

Author: Miguel Alonso Jr

.github/workflows/publish_docs.yaml

@@ -0,0 +1,24 @@
+name: Publish HTML Docs
+
+on:
+  workflow_dispatch:
+
+jobs:
+  publish:
+    name: Publish Docs to GH Pages
+    runs-on: [self-hosted, Linux, X64]
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+        with:
+          token: ${{ secrets.PUBLIC_GH_TOKEN }}
+      - name: Setup Python 3.7
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.7
+      - name: Publish docs
+        run: |
+          pip install mkdocs
+          git remote add public git@github.com:Unity-Technologies/ml-agents.git
+          mkdocs gh-deply --clean -r public
+

README.md

@@ -111,9 +111,7 @@ every step, but only when a robot arrives at a success or failure situation), is
 a defining characteristic of reinforcement learning and precisely why learning
 good policies can be difficult (and/or time-consuming) for complex environments.
 
-<p align="center">
-  <img src="images/rl_cycle.png" alt="The reinforcement learning cycle."/>
-</p>
+<div style="text-align: center"><img src="../images/rl_cycle.png" alt="The reinforcement learning lifecycle."></div>
 
 [Learning a policy](https://blogs.unity3d.com/2017/08/22/unity-ai-reinforcement-learning-with-q-learning/)
 usually requires many trials and iterative policy updates. More specifically,

docs/Background-Machine-Learning.md

@@ -0,0 +1 @@
+{!../CODE_OF_CONDUCT.md!}

docs/CODE_OF_CONDUCT.md

@@ -0,0 +1 @@
+{!../com.unity.ml-agents/CONTRIBUTING.md!}

docs/CONTRIBUTING.md

@@ -10,11 +10,11 @@ The ML-Agents Toolkit contains several components:
   contains experimental C#/Unity components that are not yet ready to be part
   of the base `com.unity.ml-agents` package. `com.unity.ml-agents.extensions`
   has a direct dependency on `com.unity.ml-agents`.
-- Three Python packages:
+- Two Python packages:
   - [`mlagents`](../ml-agents/) contains the machine learning algorithms that
     enables you to train behaviors in your Unity scene. Most users of ML-Agents
     will only need to directly install `mlagents`.
-  - [`mlagents_envs`](../ml-agents-envs/) contains a Python API to interact with
+  - [`mlagents_envs`](../ml-agents-envs/) contains a set of Python APIs to interact with
     a Unity scene. It is a foundational layer that facilitates data messaging
     between Unity scene and the Python machine learning algorithms.
     Consequently, `mlagents` depends on `mlagents_envs`.

docs/Installation.md

@@ -0,0 +1 @@
+{!../LICENSE.md!}

docs/LICENSE.md

@@ -1,6 +1,6 @@
 # Example Learning Environments
 
-<img src="images/example-envs.png" align="middle" width="3000"/>
+<img src="../images/example-envs.png" align="middle" width="3000"/>
 
 The Unity ML-Agents Toolkit includes an expanding set of example environments
 that highlight the various features of the toolkit. These environments can also

docs/Learning-Environment-Examples.md

@@ -194,5 +194,5 @@ graphics display in the Unity executable. There are two ways to achieve this:
 
 If you want to train with graphics (for example, using camera and visual observations), you'll
 need to set up display rendering support (e.g. xvfb) on you server machine. In our
-[Colab Notebook Tutorials](Readme.md#python-tutorial-with-google-colab), the Setup section has
+[Colab Notebook Tutorials](ML-Agents-Toolkit-Documentation.md#python-tutorial-with-google-colab), the Setup section has
 examples of setting up xvfb on servers.

docs/Learning-Environment-Executable.md

@@ -2,6 +2,6 @@
 
 See the package-specific Limitations pages:
 
-- [`com.unity.mlagents` Unity package](../com.unity.ml-agents/Documentation~/com.unity.ml-agents.md#known-limitations)
-- [`mlagents` Python package](../ml-agents/README.md#limitations)
-- [`mlagents_envs` Python package](../ml-agents-envs/README.md#limitations)
+- [`com.unity.mlagents` Unity package](com.unity.ml-agents.md)
+- [`mlagents` Python package](ML-Agents-README.md)
+- [`mlagents_envs` Python package](ML-Agents-Envs-README.md)