Update docs to reflect new package installation workflow. (#3362)

- Fix old material name references.
- Update outdated code comments.

.gitignore

 /envs
 
 # Environemnt logfile
-*UnitySDK.log
+*Project.log
-/UnitySDK/.vs/
+/Project/.vs/
 
 # Autogenerated VS/MD/Consulo solution and project files
 /com.unity.ml-agentsExportedObj/

docs/Installation.md

 to help you get started.
 
 ### Package Installation
+ML-Agents C# SDK is transitioning to a Unity Package.  While we are working on getting into the
+official packages list, you can add the `com.unity.ml-agents` package to your project by
+navigating to the menu `Window`  -> `Package Manager`.  In the package manager window click
+on the `+` button.
-If you intend to copy the `com.unity.ml-agents` folder in to your project, ensure that
-you have the [Barracuda preview package](https://docs.unity3d.com/Packages/com.unity.barracuda@0.3/manual/index.html) installed.
+<p align="center">
+  <img src="images/unity_package_manager_window.png"
+       alt="Linux Build Support"
+       width="500" border="10" />
+</p>
-To install the Barracuda package in later versions of Unity, navigate to the Package
-Manager window by navigating to the menu `Window` -> `Package Manager`.  Click on the
-`Advanced` dropdown menu to the left of the search bar and make sure "Show Preview Packages"
-is checked.  Search for or select the `Barracuda` package and install the latest version.
+**NOTE:** In Unity 2018.4 it's on the bottom right of the packages list, and in Unity 2019.3 it's on the top left of the packages list.
+
+Select `Add package from disk...` and navigate into the
+`com.unity.ml-agents` folder and select the `package.json` folder.
-  <img src="images/barracuda-package.png"
-       alt="Barracuda Package Manager"
-       width="710" border="10"
-       height="569" />
+  <img src="images/unity_package_json.png"
+       alt="Linux Build Support"
+       width="500" border="10" />
+
+If you are going to follow the examples from our documentation, you can open the `Project`
+folder in Unity and start tinkering immediately.
+
 
 The `ml-agents` subdirectory contains a Python package which provides deep reinforcement
 learning trainers to use with Unity environments.

docs/Learning-Environment-Create-New.md

 3. Select the Floor Plane to view its properties in the Inspector window.
 4. Set Transform to Position = (0, 0, 0), Rotation = (0, 0, 0), Scale = (1, 1, 1).
 5. On the Plane's Mesh Renderer, expand the Materials property and change the
-    default-material to *LightGridFloorSquare* (or any suitable material of your choice).
+    default-material to *GridMatFloor* (or any suitable material of your choice).
 
 (To set a new material, click the small circle icon next to the current material
 name. This opens the **Object Picker** dialog so that you can choose a
 3. Select the Target Cube to view its properties in the Inspector window.
 4. Set Transform to Position = (3, 0.5, 3), Rotation = (0, 0, 0), Scale = (1, 1, 1).
 5. On the Cube's Mesh Renderer, expand the Materials property and change the
-    default-material to *Block*.
+    default-material to *AgentBlue*.
 
 ![The Target Cube in the Inspector window](images/mlagents-NewTutBlock.png)
 
 3. Select the RollerAgent Sphere to view its properties in the Inspector window.
 4. Set Transform to Position = (0, 0.5, 0), Rotation = (0, 0, 0), Scale = (1, 1, 1).
 5. On the Sphere's Mesh Renderer, expand the Materials property and change the
-    default-material to *CheckerSquare*.
+    default-material to *Checkers_Ball*.
 6. Click **Add Component**.
 7. Add the Physics/Rigidbody component to the Sphere.
 

docs/Migrating.md

 ## Migrating from 0.13 to latest
 
 ### Important changes
+* The `UnitySDK` folder has been split into a Unity Package (`com.unity.ml-agents`) and an examples project (`Project`).  Please follow the [Intallation Guide](Installation.md) to get up and running with this new repo structure.
 * Several changes were made to how agents are reset and marked as done:
   * Calling `Done()` on the Agent will now reset it immediately and call the `AgentReset` virtual method. (This is to simplify the previous logic in which the Agent had to wait for the next `EnvironmentStep` to reset)
   * The "Reset on Done" setting in AgentParameters was removed; this is now effectively always true. `AgentOnDone` virtual method on the Agent has been removed.
 * RayPerceptionSensor was inconsistent in how it handle scale on the Agent's transform. It now scales the ray length and sphere size for casting as the transform's scale changes.
 
 ### Steps to Migrate
+* Follow the instructions on how to install the `com.unity.ml-agents` package into your project in the [Installation Guide](Installation.md).
 * If your Agent implemented `AgentOnDone` and did not have the checkbox `Reset On Done` checked in the inspector, you must call the code that was in `AgentOnDone` manually.
 * If you give your Agent a reward or penalty at the end of an episode (e.g. for reaching a goal or falling off of a platform), make sure you call `AddReward()` or `SetReward()` *before* calling `Done()`. Previously, the order didn't matter.
 * If you were not using `On Demand Decision` for your Agent, you **must** add a `DecisionRequester` component to your Agent GameObject and set its `Decision Period` field to the old `Decision Period` of the Agent.

ml-agents/tests/yamato/yamato_utils.py

 
 def run_standalone_build(base_path: str, verbose: bool = False) -> int:
     """
-    Run BuildStandalonePlayerOSX test to produce a player at UnitySDK/testPlayer
+    Run BuildStandalonePlayerOSX test to produce a player at Project/testPlayer
     :param base_path:
     :return:
     """