Minor update to docs on Brain Objects (#3029)

* Updated Brain Reference in Training ML Agents

* Removed reference to Brain object

* Update docs/Training-ML-Agents.md

Co-Authored-By: Chris Elion <chris.elion@unity3d.com>

* Update docs/Training-ML-Agents.md

Co-Authored-By: Chris Elion <chris.elion@unity3d.com>

docs/Learning-Environment-Create-New.md

     calculate the rewards used for reinforcement training. You can also implement
     optional methods to reset the Agent when it has finished or failed its task.
 4. Add your Agent subclasses to appropriate GameObjects, typically, the object
-    in the scene that represents the Agent in the simulation. Each Agent object
-    must be assigned a Brain object.
+    in the scene that represents the Agent in the simulation.
 
 **Note:** If you are unfamiliar with Unity, refer to
 [Learning the interface](https://docs.unity3d.com/Manual/LearningtheInterface.html)

docs/Training-ML-Agents.md

 The output of the training process is a model file containing the optimized
 policy. This model file is a TensorFlow data graph containing the mathematical
 operations and the optimized weights selected during the training process. You
-can use the generated model file with the Learning Brain type in your Unity
-project to decide the best course of action for an agent.
+can set the generated model file in the Behaviors Parameters under your
+Agent in your Unity project to decide the best course of action for an agent.
 
 Use the command `mlagents-learn` to train your agents. This command is installed
 with the `mlagents` package and its implementation can be found at
-each Brain.
+each Behavior.
 
 For a broader overview of reinforcement learning, imitation learning and the
 ML-Agents training process, see [ML-Agents Toolkit