Documentation tweaks and updates (#1479)

* Add blurb about using the --load flag in the intro guide, and typo fix.

* Add section in tutorial to create multiple area learning environment.

* Add mention of Done() method in agent design

docs/Getting-Started-with-Balance-Ball.md

 follow the instructions in
 [Using an Executable](Learning-Environment-Executable.md).
 
+**Note**: Re-running this command will start training from scratch again. To resume
+a previous training run, append the `--load` flag and give the same `--run-id` as the
+run you want to resume.
+
 ### Observing Training Progress
 
 Once you start training using `mlagents-learn` in the way described in the

docs/Learning-Environment-Create-New.md

 
 The default settings for the Academy properties are also fine for this
 environment, so we don't need to change anything for the RollerAcademy component
-in the Inspector window.
+in the Inspector window. You may not have the RollerBrain in the Broadcast Hub yet, 
+more on that later. 
 
 ![The Academy properties](images/mlagents-NewTutAcademy.png)
 
 you pass to the `mlagents-learn` command for each training run. If you use 
 the same id value, the statistics for multiple runs are combined and become 
 difficult to interpret.
+
+## Optional: Multiple Training Areas within the Same Scene
+
+In many of the [example environments](Learning-Environment-Examples.md), many copies of 
+the training area are instantiated in the scene. This generally speeds up training,
+allowing the environment to gather many experiences in parallel. This can be achieved
+simply by instantiating many Agents which share the same Brain. Use the following steps to
+parallelize your RollerBall environment.  
+
+### Instantiating Multiple Training Areas
+
+1. Right-click on your Project Hierarchy and create a new empty GameObject. 
+   Name it TrainingArea. 
+2. Reset the TrainingArea’s Transform so that it is at (0,0,0) with Rotation (0,0,0) 
+   and Scale (1,1,1). 
+3. Drag the Floor, Target, and RollerAgent GameObjects in the Hierarchy into the 
+   TrainingArea GameObject. 
+4. Drag the TrainingArea GameObject, along with its attached GameObjects, into your 
+   Assets browser, turning it into a prefab.
+5. You can now instantiate copies of the TrainingArea prefab. Drag them into your scene, 
+   positioning them so that they do not overlap. 
+
+### Editing the Scripts 
+
+You will notice that in the previous section, we wrote our scripts assuming that our 
+TrainingArea was at (0,0,0), performing checks such as `this.transform.position.y < 0` 
+to determine whether our agent has fallen off the platform. We will need to change 
+this if we are to use multiple TrainingAreas throughout the scene. 
+
+A quick way to adapt our current code is to use 
+localPosition rather than position, so that our position reference is in reference 
+to the prefab TrainingArea's location, and not global coordinates. 
+
+1. Replace all references of `this.transform.position` in RollerAgent.cs with `this.transform.localPosition`.
+2. Replace all references of `Target.position` in RollerAgent.cs with `Target.localPosition`.
+
+This is only one way to achieve this objective. Refer to the 
+[example environments](Learning-Environment-Examples.md) for other ways we can achieve relative positioning.
 
 ## Review: Scene Layout
 

docs/Learning-Environment-Design-Agents.md

 The `Ball3DAgent` also assigns a negative penalty when the ball falls off the
 platform.
 
+Note that all of these environments make use of the `Done()` method, which manually
+terminates an episode when a termination condition is reached. This can be 
+called independently of the `Max Step` property.
+
 ## Agent Properties
 
 ![Agent Inspector](images/agent.png)

docs/Learning-Environment-Design.md

 
 You must also determine how an Agent finishes its task or times out. You can
 manually set an Agent to done in your `AgentAction()` function when the Agent
-has finished (or irrevocably failed) its task. You can also set the Agent's `Max
-Steps` property to a positive value and the Agent will consider itself done
-after it has taken that many steps. When the Academy reaches its own `Max Steps`
-count, it starts the next episode. If you set an Agent's `ResetOnDone` property
-to true, then the Agent can attempt its task several times in one episode. (Use
-the `Agent.AgentReset()` function to prepare the Agent to start again.)
+has finished (or irrevocably failed) its task by calling the `Done()` function. 
+You can also set the Agent's `Max Steps` property to a positive value and the 
+Agent will consider itself done after it has taken that many steps. When the 
+Academy reaches its own `Max Steps` count, it starts the next episode. If you 
+set an Agent's `ResetOnDone` property to true, then the Agent can attempt its 
+task several times in one episode. (Use the `Agent.AgentReset()` function to 
+prepare the Agent to start again.)
-about programing your own Agents.
+about programming your own Agents.
 
 ## Environments