Release 0.12.1 doc fixes

docs/Installation.md

@@ -33,6 +33,27 @@ The `UnitySDK` subdirectory contains the Unity Assets to add to your projects.
 It also contains many [example environments](Learning-Environment-Examples.md)
 to help you get started.
 
+### Package Installation
+
+If you intend to copy the `UnitySDK` 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.
+
+To install the Barracuda package in Unity **2017.4.x**, you will have to copy the
+`UnityPackageManager` folder under the `UnitySDK` folder to the root directory of your
+project.
+
+To install the Barrcuda package in later versions of Unity, navigate to the Package
+Manager window by navigating to the menu `Window` -> `Package Manager`.  Click on the
+`Adavanced` 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.
+
+<p align="center">
+  <img src="images/barracuda-package.png"
+       alt="Barracuda Package Manager"
+       width="710" border="10"
+       height="569" />
+</p>
+
 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

@@ -26,8 +26,7 @@ steps:
     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/Migrating.md

@@ -22,6 +22,7 @@ The versions can be found in
 * If you use RayPerception3D, replace it with RayPerceptionSensorComponent3D (and similarly for 2D). The settings, such as ray angles and detectable tags, are configured on the component now.
 RayPerception3D would contribute `(# of rays) * (# of tags + 2)` to the State Size in Behavior Parameters, but this is no longer necessary, so you should reduce the State Size by this amount.
 Making this change will require retraining your model, since the observations that RayPerceptionSensorComponent3D produces are different from the old behavior.
+* If you see messages such as `The type or namespace 'Barracuda' could not be found` or `The type or namespace 'Google' could not be found`, you will need to [install the Barracuda preview package](Installation.md#package-installation).
 
 ## Migrating from ML-Agents toolkit v0.10 to v0.11.0
 

docs/Training-ML-Agents.md

@@ -14,15 +14,15 @@ expert in the same situation.
 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
 `ml-agents/mlagents/trainers/learn.py`. The [configuration file](#training-config-file),
 like `config/trainer_config.yaml` specifies the hyperparameters used during training.
 You can edit this file with a text editor to add a specific configuration for
-each Brain.
+each Behavior.
 
 For a broader overview of reinforcement learning, imitation learning and the
 ML-Agents training process, see [ML-Agents Toolkit

gym-unity/README.md

@@ -108,7 +108,7 @@ import gym
 from baselines import deepq
 from baselines import logger
 
-from gym_unity.envs.unity_env import UnityEnv
+from gym_unity.envs import UnityEnv
 
 def main():
     env = UnityEnv("./envs/GridWorld", 0, use_visual=True, uint8_visual=True)