Folding Broadcast Feature Into Brains

docs/Learning-Environment-Design-Brains.md

     * `Player` - Actions are decided using keyboard input mappings.
     * `Heuristic` - Actions are decided using a custom `Decision` script, which must be attached to the Brain game object.
 
+## Using the Broadcast Feature
+
+The Player, Heuristic and Internal brains have been updated to support broadcast. The broadcast feature allows you to collect data from your agents using a Python program without controlling them.  
+
+### How to use: Unity
+
+To turn it on in Unity, simply check the `Broadcast` box as shown bellow:
+
+![Broadcast](images/broadcast.png)
+
+### How to use: Python 
+
+When you launch your Unity Environment from a Python program, you can see what the agents connected to non-external brains are doing. When calling `step` or `reset` on your environment, you retrieve a dictionary mapping brain names to `BrainInfo` objects. The  dictionary contains a `BrainInfo` object for each non-external brain set to broadcast as well as for any external brains.  
+
+Just like with an external brain, the `BrainInfo` object contains the fields for `visual_observations`, `vector_observations`,  `text_observations`, `memories`,`rewards`, `local_done`, `max_reached`, `agents` and `previous_actions`. Note that `previous_actions` corresponds to the actions that were taken by the agents at the previous step, not the current one.  
+
+Note that when you do a `step` on the environment, you cannot provide actions for non-external brains. If there are no external brains in the scene, simply call `step()` with no arguments.  
+
+You can use the broadcast feature to collect data generated by Player, Heuristics or Internal brains game sessions. You can then use this data to train an agent in a supervised context.
 
 

docs/ML-Agents-Overview.md

 Player Brain are used to learn the policies of an agent through demonstration.
 However, this could also be helpful for the Heuristic and Internal Brains,
 particularly when debugging agent behaviors. You can learn more about using 
-the broadcasting feature [here](Feature-Broadcasting.md).
+the broadcasting feature 
+[here](Learning-Environment-Design-Brains.md#using-the-broadcast-feature).
 
 * **Docker Set-up (Experimental)** - To facilitate setting up ML-Agents
 without installing Python or TensorFlow directly, we provide a 

docs/Feature-Broadcasting.md

-# Using the Broadcast Feature
-
-The Player, Heuristic and Internal brains have been updated to support broadcast. The broadcast feature allows you to collect data from your agents using a Python program without controlling them.  
-
-## How to use : Unity
-
-To turn it on in Unity, simply check the `Broadcast` box as shown bellow:
-
-![Broadcast](images/broadcast.png)
-
-## How to use : Python 
-
-When you launch your Unity Environment from a Python program, you can see what the agents connected to non-external brains are doing. When calling `step` or `reset` on your environment, you retrieve a dictionary mapping brain names to `BrainInfo` objects. The  dictionary contains a `BrainInfo` object for each non-external brain set to broadcast as well as for any external brains.  
-
-Just like with an external brain, the `BrainInfo` object contains the fields for `visual_observations`, `vector_observations`,  `text_observations`, `memories`,`rewards`, `local_done`, `max_reached`, `agents` and `previous_actions`. Note that `previous_actions` corresponds to the actions that were taken by the agents at the previous step, not the current one.  
-
-Note that when you do a `step` on the environment, you cannot provide actions for non-external brains. If there are no external brains in the scene, simply call `step()` with no arguments.  
-
-You can use the broadcast feature to collect data generated by Player, Heuristics or Internal brains game sessions. You can then use this data to train an agent in a supervised context.