Change docs and ipynb to reflect new import path for UnityEnvironment (#2673)

Author: Ervin T

docs/Learning-Environment-Executable.md

@@ -31,8 +31,8 @@ Make sure the Brains in the scene have the right type. For example, if you want
 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
@@ -73,7 +73,7 @@ executable, you can pass the name of the executable with the argument
 '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/Migrating.md

@@ -10,6 +10,7 @@
 * `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

docs/Python-API.md

@@ -16,7 +16,7 @@ imitation learning.
 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:
@@ -52,7 +52,7 @@ file, put the file in the same directory as `envs`. For example, if the filename
 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)
 ```
 
@@ -94,14 +94,14 @@ A BrainInfo object contains the following fields:
   `(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)`**
   Send a reset signal to the environment, and provides a dictionary mapping
-  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
@@ -110,7 +110,7 @@ variable named `env` in this example, can be used in the following way:
     `resetParameters` and the values are their corresponding float values.
     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.
@@ -141,7 +141,7 @@ variable named `env` in this example, can be used in the following way:
     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/Training-on-Amazon-Web-Service.md

@@ -7,11 +7,11 @@ Service for training ML-Agents environments.
 
 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
@@ -102,14 +102,14 @@ linux executables which use visual observations.
 
     # 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:
 
     ```sh
     # Download and install the latest Nvidia driver for ubuntu
-    # 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
 
@@ -138,7 +138,7 @@ linux executables which use visual observations.
    # 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                    |
@@ -156,7 +156,7 @@ linux executables which use visual observations.
    # |=============================================================================|
    # |  No running processes found                                                 |
    # +-----------------------------------------------------------------------------+
-   
+
    ```
 
 #### Start X Server and make the ubuntu use X Server for display:
@@ -179,21 +179,21 @@ linux executables which use visual observations.
     # 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
-    
+
     # Running synchronized to the vertical refresh.  The framerate should be
     # approximately the same as the monitor refresh rate.
     # 137296 frames in 5.0 seconds = 27459.053 FPS
     # 141674 frames in 5.0 seconds = 28334.779 FPS
     # 141490 frames in 5.0 seconds = 28297.875 FPS
-    
+
     ```
 
 ## Training on EC2 instance
 
 1. In the Unity Editor, load a project containing an ML-Agents environment (you
    can use one of the example environments if you have not created your own).
 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.)
@@ -220,7 +220,7 @@ 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>)
     ```
@@ -233,12 +233,12 @@ Headless Mode, you have to setup the X Server to enable training.)
     ```console
     mlagents-learn <trainer-config-file> --env=<your_env> --train
     ```
-    
+
 ## FAQ
 
 ### The <Executable_Name>_Data folder hasn't been copied cover
 
-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
@@ -269,7 +269,7 @@ mlagents.envs.exception.UnityTimeOutException: The Unity environment took too lo
          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
 
@@ -310,4 +310,4 @@ You might see something like:
 ```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

@@ -45,7 +45,7 @@ To verify that all steps worked correctly:
 7. Test the instance setup from Python using:
 
 ```python
-from mlagents.envs import UnityEnvironment
+from mlagents.envs.environment import UnityEnvironment
 
 env = UnityEnvironment(<your_env>)
 ```
@@ -79,9 +79,9 @@ the GPU is being used by running `nvidia-smi` from the command line.
 ## 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.

notebooks/getting-started.ipynb

@@ -47,7 +47,7 @@
     "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",