Edit migrating doc

notebooks/getting-started.ipynb

     "import numpy as np\n",
     "import sys\n",
     "\n",
-    "from mlagents.envs import UnityEnvironment\n",
+    "from mlagents.envs.environment import UnityEnvironment\n",
     "\n",
     "%matplotlib inline\n",
     "\n",

docs/Python-API.md

 The ML-Agents Toolkit provides a Python API for controlling the Agent simulation
 loop of an environment or game built with Unity. This API is used by the
 training algorithms inside the ML-Agent Toolkit, but you can also write your own
-Python programs using this API. Go [here](../notebooks/getting-started.ipynb) 
+Python programs using this API. Go [here](../notebooks/getting-started.ipynb)
 for a Jupyter Notebook walking through the functionality of the API.
 
 The key objects in the Python API include:
 of your Unity environment is 3DBall.app, in python, run:
 
 ```python
-from mlagents.envs import UnityEnvironment
+from mlagents.envs.environment import UnityEnvironment
 env = UnityEnvironment(file_name="3DBall", worker_id=0, seed=1)
 ```
 
   `(batch size, number of branches)` if the vector action space is discrete.
 
 Once loaded, you can use your UnityEnvironment object, which referenced by a
-variable named `env` in this example, can be used in the following way:  
+variable named `env` in this example, can be used in the following way:
-- **Print : `print(str(env))`**  
-  Prints all parameters relevant to the loaded environment and the 
-  Brains.  
-- **Reset : `env.reset(train_model=True, config=None)`**  
+- **Print : `print(str(env))`**
+  Prints all parameters relevant to the loaded environment and the
+  Brains.
+- **Reset : `env.reset(train_model=True, config=None)`**
-  Brain names to BrainInfo objects.  
+  Brain names to BrainInfo objects.
   - `train_model` indicates whether to run the environment in train (`True`) or
     test (`False`) mode.
   - `config` is an optional dictionary of configuration flags specific to the
     Define the reset parameters on the Academy Inspector window in the Unity
     Editor.
-- **Step : `env.step(action, memory=None, text_action=None)`**  
+- **Step : `env.step(action, memory=None, text_action=None)`**
   Sends a step signal to the environment using the actions. For each Brain :
   - `action` can be one dimensional arrays or two dimensional arrays if you have
     multiple Agents per Brain.
     action = {'brain1':[1.0, 2.0], 'brain2':[3.0,4.0]}
     ```
 
-    Returns a dictionary mapping Brain names to BrainInfo objects.  
+    Returns a dictionary mapping Brain names to BrainInfo objects.
 - **Close : `env.close()`**
   Sends a shutdown signal to the environment and closes the communication
   socket.

docs/Learning-Environment-Executable.md

 to be able to control your agents from Python, you will need to put the Brain
 controlling the Agents to be a **Learning Brain** and drag it into the
 Academy's `Broadcast Hub` with the `Control` checkbox checked. In the 3DBall
-scene, this can be done in the Platform GameObject within the Game prefab in 
-`Assets/ML-Agents/Examples/3DBall/Prefabs/`, or in each instance of the 
+scene, this can be done in the Platform GameObject within the Game prefab in
+`Assets/ML-Agents/Examples/3DBall/Prefabs/`, or in each instance of the
 Platform in the Scene.
 
 Next, we want the set up scene to play correctly when the training process
 'file_name' of the `UnityEnvironment`. For instance:
 
 ```python
-from mlagents.envs import UnityEnvironment
+from mlagents.envs.environment import UnityEnvironment
 env = UnityEnvironment(file_name=<env_name>)
 ```
 

docs/Training-on-Amazon-Web-Service.md

 
 We've prepared a pre-configured AMI for you with the ID: `ami-016ff5559334f8619` in the
 `us-east-1` region. It was created as a modification of [Deep Learning AMI
-(Ubuntu)](https://aws.amazon.com/marketplace/pp/B077GCH38C). The AMI has been 
-tested with p2.xlarge instance. Furthermore, if you want to train without 
-headless mode, you need to enable X Server. 
+(Ubuntu)](https://aws.amazon.com/marketplace/pp/B077GCH38C). The AMI has been
+tested with p2.xlarge instance. Furthermore, if you want to train without
+headless mode, you need to enable X Server.
-After launching your EC2 instance using the ami and ssh into it, run the 
+After launching your EC2 instance using the ami and ssh into it, run the
 following commands to enable it:
 
 ```sh
 
     # Remove the Section "Files" from the /etc/X11/xorg.conf file
     # And remove two lines that contain Section "Files" and EndSection
-    $ sudo vim /etc/X11/xorg.conf 
+    $ sudo vim /etc/X11/xorg.conf
     ```
 
 #### Update and setup Nvidia driver:
-    # Please refer to http://download.nvidia.com/XFree86/Linux-#x86_64/latest.txt     
+    # Please refer to http://download.nvidia.com/XFree86/Linux-#x86_64/latest.txt
     $ wget http://download.nvidia.com/XFree86/Linux-x86_64/390.87/NVIDIA-Linux-x86_64-390.87.run
     $ sudo /bin/bash ./NVIDIA-Linux-x86_64-390.67.run --accept-license --no-questions --ui=none
 
    # You will have a list of processes running on the GPU, Xorg should not be in
    # the list, as shown below.
    $ nvidia-smi
-   
+
    # Thu Jun 14 20:21:11 2018
    # +-----------------------------------------------------------------------------+
    # | NVIDIA-SMI 390.67                 Driver Version: 390.67                    |
    # |=============================================================================|
    # |  No running processes found                                                 |
    # +-----------------------------------------------------------------------------+
-   
+
    ```
 
 #### Start X Server and make the ubuntu use X Server for display:
     # For more information on glxgears, see ftp://www.x.org/pub/X11R6.8.1/doc/glxgears.1.html.
     $ glxgears
     # If Xorg is configured correctly, you should see the following message
-    
+
-    
+
     ```
 
 ## Training on EC2 instance
 2. Open the Build Settings window (menu: File > Build Settings).
-3. Select Linux as the Target Platform, and x86_64 as the target architecture 
+3. Select Linux as the Target Platform, and x86_64 as the target architecture
 (the default x86 currently does not work).
 4. Check Headless Mode if you have not setup the X Server. (If you do not use
 Headless Mode, you have to setup the X Server to enable training.)
 9. Test the instance setup from Python using:
 
     ```python
-    from mlagents.envs import UnityEnvironment
+    from mlagents.envs.environment import UnityEnvironment
 
     env = UnityEnvironment(<your_env>)
     ```
     ```console
     mlagents-learn <trainer-config-file> --env=<your_env> --train
     ```
-    
+
-If you've built your Linux executable, but forget to copy over the corresponding <Executable_Name>_Data folder, you will see error message like the following: 
+If you've built your Linux executable, but forget to copy over the corresponding <Executable_Name>_Data folder, you will see error message like the following:
 
 ```sh
 Set current directory to /home/ubuntu/ml-agents/ml-agents
          The environment and the Python interface have compatible versions.
 ```
 
-It would be also really helpful to check your /home/ubuntu/.config/unity3d/<Some_Path>/Player.log to see what happens with your Unity environment. 
+It would be also really helpful to check your /home/ubuntu/.config/unity3d/<Some_Path>/Player.log to see what happens with your Unity environment.
 
 ### Could not launch X Server
 
 ```sh
 NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.
 ```
-This means the NVIDIA's driver needs to be updated. Refer to [this section](Training-on-Amazon-Web-Service.md#update-and-setup-nvidia-driver) for more information. 
+This means the NVIDIA's driver needs to be updated. Refer to [this section](Training-on-Amazon-Web-Service.md#update-and-setup-nvidia-driver) for more information.

docs/Training-on-Microsoft-Azure.md

 7. Test the instance setup from Python using:
 
 ```python
-from mlagents.envs import UnityEnvironment
+from mlagents.envs.environment import UnityEnvironment
 
 env = UnityEnvironment(<your_env>)
 ```
 ## Monitoring your Training Run with TensorBoard
 
 Once you have started training, you can [use TensorBoard to observe the
-training](Using-Tensorboard.md).  
+training](Using-Tensorboard.md).
-1. Start by [opening the appropriate port for web traffic to connect to your VM](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/nsg-quickstart-portal).  
+1. Start by [opening the appropriate port for web traffic to connect to your VM](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/nsg-quickstart-portal).
 
     * Note that you don't need to generate a new `Network Security Group` but
       instead, go to the **Networking** tab under **Settings** for your VM.

docs/Migrating.md

 * `UnitySDK/Assets/ML-Agents/Scripts/Communicator.cs` and its class `Communicator` have been renamed to `UnitySDK/Assets/ML-Agents/Scripts/ICommunicator.cs` and `ICommunicator` respectively.
 * The `SpaceType` Enums `discrete`, and `continuous` have been renamed to `Discrete` and `Continuous`.
 * We have removed the `Done` call as well as the capacity to set `Max Steps` on the Academy. Therefore an AcademyReset will never be triggered from C# (only from Python). If you want to reset the simulation after a fixed number of steps, or when an event in the simulation occurs, we recommend looking at our multi-agent example environments (such as BananaCollector). In our examples, groups of Agents can be reset through an "Area" that can reset groups of Agents.
+* The import for `mlagents.envs.UnityEnvironment` was removed. If you are using the Python API, change `from mlagents.envs import UnityEnvironment` to `from mlagents.envs.environment import UnityEnvironment`.
 
 
 ## Migrating from ML-Agents toolkit v0.8 to v0.9