documentation touchups (#4099)

* doc updates

getting started page now uses consistent run-id

re-order create-new docs to have less back/forth between unity and text editor

* add link explaining decisions where we tell the reader to modify its parameter

docs/Getting-Started.md

 run the same command again, appending the `--resume` flag:
 
 ```sh
-mlagents-learn config/ppo/3DBall.yaml --run-id=firstRun --resume
+mlagents-learn config/ppo/3DBall.yaml --run-id=first3DBallRun --resume
 ```
 
 Your trained model will be at `results/<run-identifier>/<behavior_name>.nn` where

docs/Learning-Environment-Create-New.md

 Vector3 controlSignal = Vector3.zero;
 controlSignal.x = action[0];
 controlSignal.z = action[1];
-rBody.AddForce(controlSignal * speed);
+rBody.AddForce(controlSignal * forceMultiplier);
 ```
 
 #### Rewards
 `OnActionReceived()` function looks like:
 
 ```csharp
-public float speed = 10;
+public float forceMultiplier = 10;
 public override void OnActionReceived(float[] vectorAction)
 {
     // Actions, size = 2
-    rBody.AddForce(controlSignal * speed);
+    rBody.AddForce(controlSignal * forceMultiplier);
 
     // Rewards
     float distanceToTarget = Vector3.Distance(this.transform.localPosition, Target.localPosition);
 }
 ```
 
-Note the `speed` class variable is defined before the function. Since `speed` is
+Note the `forceMultiplier` class variable is defined before the function. Since `forceMultiplier` is
-## Final Editor Setup
-
-Now, that all the GameObjects and ML-Agent components are in place, it is time
-to connect everything together in the Unity Editor. This involves changing some
-of the Agent Component's properties so that they are compatible with our Agent
-code.
-
-1. Select the **RollerAgent** GameObject to show its properties in the Inspector
-   window.
-1. Add the `Decision Requester` script with the Add Component button from the
-   RollerAgent Inspector.
-1. Change **Decision Period** to `10`.
-1. Drag the Target GameObject from the Hierarchy window to the RollerAgent
-   Target field.
-1. Add the `Behavior Parameters` script with the Add Component button from the
-   RollerAgent Inspector.
-1. Modify the Behavior Parameters of the Agent :
-   - `Behavior Name` to _RollerBall_
-   - `Vector Observation` > `Space Size` = 8
-   - `Vector Action` > `Space Type` = **Continuous**
-   - `Vector Action` > `Space Size` = 2
-
-Now you are ready to test the environment before training.
-
 ## Testing the Environment
 
 It is always a good idea to first test your environment by controlling the Agent
 Console window and that the Agent resets when it reaches its target or falls
 from the platform.
 
+## Final Editor Setup
+
+Now, that all the GameObjects and ML-Agent components are in place, it is time
+to connect everything together in the Unity Editor. This involves changing some
+of the Agent Component's properties so that they are compatible with our Agent
+code.
+
+1. Select the **RollerAgent** GameObject to show its properties in the Inspector
+   window.
+1. Add the `Decision Requester` script with the Add Component button from the
+   RollerAgent Inspector.
+1. Change **Decision Period** to `10`. For more information on decisions, see [the Agent documentation](Learning-Environment-Design-Agents.md#decisions)
+1. Drag the Target GameObject from the Hierarchy window to the RollerAgent
+   Target field.
+1. Add the `Behavior Parameters` script with the Add Component button from the
+   RollerAgent Inspector.
+1. Modify the Behavior Parameters of the Agent :
+   - `Behavior Name` to _RollerBall_
+   - `Vector Observation` > `Space Size` = 8
+   - `Vector Action` > `Space Type` = **Continuous**
+   - `Vector Action` > `Space Size` = 2
+
+Now you are ready to test the environment before training.
+
 ## Training the Environment
 
 The process is the same as described in the
     time_horizon: 64
     summary_freq: 10000
 ```
+
+Hyperparameters are explained in [the training configuration file documentation](Training-Configuration-File.md)
 
 Since this example creates a very simple training environment with only a few
 inputs and outputs, using small batch and buffer sizes speeds up the training