Update Installation for windows

docs/Getting-Started-with-Balance-Ball.md

@@ -272,6 +272,7 @@ To summarize, go to your command line, enter the `ml-agents` directory and type:
 ```python
 python3 python/learn.py <env_file_path> --run-id=<run-identifier> --train 
 ```
+**Note**: If you're using Anaconda, don't forget to activate the ml-agents environment first.
 
 The `--train` flag tells ML-Agents to run in training mode. `env_file_path` should be the path to the Unity executable that was just created. 
 
@@ -316,6 +317,7 @@ during a successful training session.
 Once the training process completes, and the training process saves the model 
 (denoted by the `Saved Model` message) you can add it to the Unity project and 
 use it with agents having an **Internal** brain type.
+**Note:** Do not just close the Unity Window once the `Saved Model` message appears. Either wait for the training process to close the window or press Ctrl+C at the command-line prompt. If you simply close the window manually, the .bytes file containing the trained model is not exported into the ml-agents folder.
 
 ### Setting up TensorFlowSharp Support
 

docs/Installation-Windows.md

@@ -6,7 +6,7 @@ To use ML-Agents, you install Python and the required Python packages as outline
 
 ## Step 1: Install Python via Anaconda
 
-[Download](https://www.anaconda.com/download/#windows) and install Anaconda for Windows. By using Anaconda, you can manage separate environments for different distributions of Python. Python 3 is required as we no longer support Python 2. In this guide, we are using Python version 3.6 and Anaconda version 5.1 ([64-bit](https://repo.continuum.io/archive/Anaconda3-5.1.0-Windows-x86_64.exe) or [32-bit](https://repo.continuum.io/archive/Anaconda3-5.1.0-Windows-x86.exe) direct links).
+[Download](https://www.anaconda.com/download/#windows) and install Anaconda for Windows. By using Anaconda, you can manage separate environments for different distributions of Python. Python 3 is required as we no longer support Python 2. In this guide, we are using Python version 3.5 (Anaconda comes with Python 3.6, we will create an environment with python 3.5) and Anaconda version 5.1 ([64-bit](https://repo.continuum.io/archive/Anaconda3-5.1.0-Windows-x86_64.exe) or [32-bit](https://repo.continuum.io/archive/Anaconda3-5.1.0-Windows-x86.exe) direct links).
 
 <p align="center">
     <img src="images/anaconda_install.PNG" 
@@ -23,15 +23,21 @@ We recommend the default _advanced installation options_. However, select the op
 </p>
 
 After installation, you must open __Anaconda Navigator__ to finish the setup. From the Windows search bar, type _anaconda navigator_. You can close Anaconda Navigator after it opens.
-
+If environment variables were not created, or if you see the error "conda is not recognized as internal or external command", in System Variables, "Path" add the following new paths:
+```
+    C:\ProgramData\Anaconda3\Scripts
+    C:\ProgramData\Anaconda3\Scripts\conda.exe
+    C:\ProgramData\Anaconda3
+    C:\ProgramData\Anaconda3\python.exe
+```
 ## Step 2: Setup and Activate a New Conda Environment
 
 You will create a new [Conda environment](https://conda.io/docs/) to be used with ML-Agents. This means that all the packages that you install are localized to just this environment. It will not affect any other installation of Python or other environments. Whenever you want to run ML-Agents, you will need activate this Conda environment.
 
 To create a new Conda environment, open a new Anaconda Prompt (_Anaconda Prompt_ in the search bar) and type in the following command:
 
 ```
-conda create -n ml-agents python=3.6
+conda create -n ml-agents python=3.5
 ```
 
 You may be asked to install new packages. Type `y` and press enter _(make sure you are connected to the internet)_. You must install these required packages. The new Conda environment is called ml-agents and uses Python version 3.6.
@@ -50,10 +56,10 @@ conda activate ml-agents
 
 You should see `(ml-agents)` prepended on the last line.
 
-Next, install `tensorflow`. Install this package using `pip` - which is a package management system used to install Python packages. In the same Anaconda Prompt, type in the following command _(make sure you are connected to the internet)_:
+Next, install `tensorflow`. Install this package using `pip` - which is a package management system used to install Python packages. Latest versions of Tensorflow won't work, so you will need to make sure that you install version 1.4.0. In the same Anaconda Prompt, type in the following command _(make sure you are connected to the internet)_:
 
 ```
-pip install tensorflow
+pip install tensorflow==1.4.0
 ```
 
 ## Step 3: Install Required Python Packages
@@ -78,6 +84,7 @@ Make sure you are connected to the internet and then type in the Anaconda Prompt
 
 ```
 pip install .
+
 ```
 
 This will complete the installation of all the required Python packages to run ML-Agents. 
@@ -90,7 +97,7 @@ As of ML-Agents v0.3, only CUDA 8 and cuDNN 6 is supported.
 
 ### Install Nvidia CUDA toolkit
 
-[Download](https://developer.nvidia.com/cuda-toolkit-archive) and install the CUDA toolkit from Nvidia's archive. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler and a runtime library and is needed to run ML-Agents. In this guide, we are using version 8.0.61 ([direct link](https://developer.nvidia.com/compute/cuda/8.0/Prod2/network_installers/cuda_8.0.61_win10_network-exe)). 
+[Download](https://developer.nvidia.com/cuda-toolkit-archive) and install the CUDA toolkit from Nvidia's archive. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ (Step Visual Studio 2015) compiler and a runtime library and is needed to run ML-Agents. In this guide, we are using version 8.0.61 ([direct link](https://developer.nvidia.com/compute/cuda/8.0/Prod2/network_installers/cuda_8.0.61_win10_network-exe)). 
 
 Before installing, please make sure you __close any running instances of Unity or Visual Studio__.
 
@@ -169,7 +176,7 @@ Make sure to replace the relevant directory location with the one you have insta
 Next, install `tensorflow-gpu` using `pip`. In an Anaconda Prompt with the Conda environment ml-agents activated, type in the following command _(make sure you are connected to the internet)_:
 
 ```
-pip install tensorflow-gpu
+pip install tensorflow-gpu==1.4.0
 ```
 
 Lastly, you should test to see if everything installed properly and that TensorFlow can identify your GPU. In the same Anaconda Prompt, type in the following command:
@@ -186,6 +193,11 @@ You should see something similar to:
 Found device 0 with properties ...
 ```
 
+Step Visual Studio 2015: CUDA 8.0 is not compatible with Visual Studio 2017, so you will need an older version. Uninstall Visual Studio 2017 that comes with Unity, download Visual Studio Enterprise 2015 and install it with the Windows SDK. If you don't want/ can't install Visual Studio Enterprise 2015, you will need: 
+    [Visual C++ Redistributable for Visual Studio 2015](https://www.microsoft.com/en-us/download/details.aspx?id=48145),
+    [Visual Studio Community 2015](https://www.visualstudio.com/vs/older-downloads/) + [Windows SDK](https://msdn.microsoft.com/en-us/library/mt683786.aspx).
+
+
 ## Acknowledgements
 
 We would like to thank [Jason Weimann](https://unity3d.college/2017/10/25/machine-learning-in-unity3d-setting-up-the-environment-tensorflow-for-agentml-on-windows-10/) and [Nitish S. Mutha](http://blog.nitishmutha.com/tensorflow/2017/01/22/TensorFlow-with-gpu-for-windows.html) for writing the original articles which were used to create this guide.