Minor fixes to Windows Installation guide

- Reverted links to standard markdown for consistency
- Removed double-spaces post periods
- Removed optional headers for GPU step.

Author: Marwan Mattar

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
 
-<a href="https://www.anaconda.com/download/#windows" target="_blank">Download</a> 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 3.6 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).
 
 <p align="center">
     <img src="images/anaconda_install.PNG" 
@@ -26,7 +26,7 @@ After installation, you must open __Anaconda Navigator__ to finish the setup. Fr
 
 ## 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.
+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:
 
@@ -68,7 +68,7 @@ git clone [email protected]:Unity-Technologies/ml-agents.git
 
 If you don't want to use Git, you can always directly download all the files [here](https://github.com/Unity-Technologies/ml-agents/archive/master.zip).
 
-In our example, the files are located in `C:\Downloads`.  After you have either cloned or downloaded the files, from the Anaconda Prompt, change to the python directory inside the ML-agents directory:
+In our example, the files are located in `C:\Downloads`. After you have either cloned or downloaded the files, from the Anaconda Prompt, change to the python directory inside the ML-agents directory:
 
 ```
 cd C:\Downloads\ml-agents\python
@@ -80,92 +80,92 @@ 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.  
+This will complete the installation of all the required Python packages to run ML-Agents. 
 
-## (Optional) GPU Training using ML-Agents 
+## (Optional) Step 4: GPU Training using ML-Agents 
 
-Not required to use v0.3 for ML-Agents.  This is a guide for advanced users who want to train using GPUs.  Additionally, you will need to check if your GPU is CUDA compatible.  Please check Nvidia's page [here](https://developer.nvidia.com/cuda-gpus).
+Not required to use v0.3 for ML-Agents. This is a guide for advanced users who want to train using GPUs. Additionally, you will need to check if your GPU is CUDA compatible. Please check Nvidia's page [here](https://developer.nvidia.com/cuda-gpus).
 
 As of ML-Agents v0.3, only CUDA 8 and cuDNN 6 is supported.
 
-## (Optional) Step 1: Install Nvidia CUDA toolkit
+### Install Nvidia CUDA toolkit
 
-<a href="https://developer.nvidia.com/cuda-toolkit-archive" target="_blank">Download</a> 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++ 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.___
+Before installing, please make sure you __close any running instances of Unity or Visual Studio__.
 
-Run the installer and select the Express option.  Note the directory where you installed the CUDA toolkit.  In this guide, we installed in the directory `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0`
+Run the installer and select the Express option. Note the directory where you installed the CUDA toolkit. In this guide, we installed in the directory `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0`
 
-## (Optional) Step 2: Install Nvidia cuDNN library
+### Install Nvidia cuDNN library
 
-<a href="https://developer.nvidia.com/cudnn" target="_blank">Download</a> and install the cuDNN library from Nvidia.  cuDNN is is a GPU-accelerated library of primitives for deep neural networks.  Before you can download, you will need to sign up for free to the Nvidia Developer Program.
+[Download](https://developer.nvidia.com/cudnn) and install the cuDNN library from Nvidia. cuDNN is is a GPU-accelerated library of primitives for deep neural networks. Before you can download, you will need to sign up for free to the Nvidia Developer Program.
 
 <p align="center">
     <img src="images/cuDNN_membership_required.png" 
         alt="cuDNN membership required" 
         width="500" border="10" />
 </p>
 
-Once you've signed up, go back to the cuDNN <a href="https://developer.nvidia.com/cudnn" target="_blank">downloads page</a>.  You may or may not be asked to fill out a short survey.  When you get to the list cuDNN releases, __make sure you are downloading the right version for the CUDA toolkit you installed in Step 1.__  In this guide, we are using version 6.0 for CUDA toolkit version 8.0 ([direct link](https://developer.nvidia.com/compute/machine-learning/cudnn/secure/v6/prod/8.0_20170307/cudnn-8.0-windows10-x64-v6.0-zip)).
+Once you've signed up, go back to the cuDNN [downloads page](https://developer.nvidia.com/cudnn). You may or may not be asked to fill out a short survey. When you get to the list cuDNN releases, __make sure you are downloading the right version for the CUDA toolkit you installed in Step 1.__  In this guide, we are using version 6.0 for CUDA toolkit version 8.0 ([direct link](https://developer.nvidia.com/compute/machine-learning/cudnn/secure/v6/prod/8.0_20170307/cudnn-8.0-windows10-x64-v6.0-zip)).
 
-After you have downloaded the cuDNN files, you will need to extract the files into the CUDA toolkit directory.  In the cuDNN zip file, there are three folders called `bin`, `include`, and `lib`.  
+After you have downloaded the cuDNN files, you will need to extract the files into the CUDA toolkit directory. In the cuDNN zip file, there are three folders called `bin`, `include`, and `lib`. 
 
 <p align="center">
     <img src="images/cudnn_zip_files.PNG" 
         alt="cuDNN zip files" 
         width="500" border="10" />
 </p>
 
-Copy these three folders into the CUDA toolkit directory.  The CUDA toolkit directory is located at `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0`
+Copy these three folders into the CUDA toolkit directory. The CUDA toolkit directory is located at `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0`
 
 <p align="center">
     <img src="images/cuda_toolkit_directory.PNG" 
         alt="cuda toolkit directory" 
         width="500" border="10" />
 </p>
 
-## (Optional) Step 3: Set Environment Variables
+### Set Environment Variables
 
 You will need to add one environment variable and two path variables.
 
-To set the environment variable, type `environment variables` in the search bar (this can be reached by hitting the Windows key or the bottom left Windows button).  You should see an option called __Edit the system environment variables__.  
+To set the environment variable, type `environment variables` in the search bar (this can be reached by hitting the Windows key or the bottom left Windows button). You should see an option called __Edit the system environment variables__. 
 
 <p align="center">
     <img src="images/edit_env_var.png" 
         alt="edit env variables" 
         width="250" border="10" />
 </p>
 
-From here, click the __Environment Variables__ button.  Click __New__ to add a new system variable _(make sure you do this under __System variables__ and not User variables_.  
+From here, click the __Environment Variables__ button. Click __New__ to add a new system variable _(make sure you do this under __System variables__ and not User variables_. 
 
 <p align="center">
     <img src="images/new_system_variable.PNG" 
         alt="new system variable" 
         width="500" border="10" />
 </p>
 
-For __Variable Name__, enter `CUDA_HOME`.  For the variable value, put the directory location for the CUDA toolkit.  In this guide, the directory location is `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0`.  Press __OK__ once.
+For __Variable Name__, enter `CUDA_HOME`. For the variable value, put the directory location for the CUDA toolkit. In this guide, the directory location is `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0`. Press __OK__ once.
 
 <p align="center">
     <img src="images/system_variable_name_value.PNG" 
         alt="system variable names and values" 
         width="500" border="10" />
 </p>
 
-To set the two path variables, inside the same __Environment Variables__ window and under the second box called __System Variables__, find a variable called `PATH` and click __Edit__.  You will add two directories to the list.  For this guide, the two entries would look like:
+To set the two path variables, inside the same __Environment Variables__ window and under the second box called __System Variables__, find a variable called `PATH` and click __Edit__. You will add two directories to the list. For this guide, the two entries would look like:
 
     C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\lib\x64
     C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\extras\CUPTI\libx64
 
-Make sure to replace the relevant directory location with the one you have installed.  _Please note that case sensitivity matters_.
+Make sure to replace the relevant directory location with the one you have installed. _Please note that case sensitivity matters_.
 
 <p align="center">
     <img src="images/path_variables.PNG" 
         alt="Path variables" 
         width="500" border="10" />
 </p>
 
-## (Optional) Step 4: Install TensorFlow GPU
+### Install TensorFlow GPU
 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)_:
 
 ```