pull/1294 from has-taiar (#1310)

* pull/1294 from has-taiar

* removed the left bracket

* moved the windows link position

* update the windows doc

* resolved the comments, changed the pip install . to pip install -e . , added the package explanation to the Windows installation doc

* Resolved the comments

* add the 'the'

docs/Installation-Windows.md

 
 To use the ML-Agents toolkit, you install Python and the required Python
 packages as outlined below. This guide also covers how set up GPU-based training
-(for advanced users). GPU-based training is not required for the v0.4 release of
-the ML-Agents toolkit. However, training on a GPU might be required by future
+(for advanced users). GPU-based training is not currently required for the 
+ML-Agents toolkit. However, training on a GPU might be required by future
 versions and features.
 
 ## Step 1: Install Python via Anaconda
 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).
 
+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.
+
+The `ml-agents` subdirectory contains Python packages which provide
+trainers and a Python API to interface with Unity.
+
+The `gym-unity` subdirectory contains a package to interface with OpenAI Gym.
+
-cloned or downloaded the files, from the Anaconda Prompt, change to the python
-directory inside the ml-agents directory:
+cloned or downloaded the files, from the Anaconda Prompt, change to the ml-agents
+subdirectory inside the ml-agents directory:
 
 ```console
 cd C:\Downloads\ml-agents\ml-agents
-Prompt:
+Prompt within `ml-agents` subdirectory:
-pip install .
+pip install -e .
 ```
 
 This will complete the installation of all the required Python packages to run
 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 the ML-Agents toolkit v0.4, only CUDA v9.0 and cuDNN v7.0.5 is supported.
+Currently for the ML-Agents toolkit, only CUDA v9.0 and cuDNN v7.0.5 is supported.
 
 ### Install Nvidia CUDA toolkit
 

docs/Installation.md

        width="500" border="10" />
 </p>
 
-## Clone the ML-Agents Toolkit Repository
+## Windows Users
+For setting up your environment on Windows, we have created a [detailed
+guide](Installation-Windows.md) to setting up your env. For Mac and Linux,
+continue with this guide.
+
+## Mac and Unix Users
+
+### Clone the ML-Agents Toolkit Repository
 
 Once installed, you will want to clone the ML-Agents Toolkit GitHub repository.
 
 
 The `UnitySDK` subdirectory contains the Unity Assets to add to your projects.
 It also contains many [example environments](Learning-Environment-Examples.md)
-that can be used to help get you familiar with Unity.
+to help you get started.
 
 The `ml-agents` subdirectory contains Python packages which provide
 trainers and a Python API to interface with Unity.
-## Install Python and mlagents Package
+### Install Python and mlagents Package
 
 In order to use ML-Agents toolkit, you need Python 3.6 along with the
 dependencies listed in the [requirements file](../ml-agents/requirements.txt).
 - [Jupyter](Background-Jupyter.md)
 
-### NOTES
-
-- We do not currently support Python 3.7 or Python 3.5.
-- If you are using Anaconda and are having trouble with TensorFlow, please see
-  the following
-  [note](https://www.tensorflow.org/install/install_mac#installing_with_anaconda)
-  on how to install TensorFlow in an Anaconda environment.
-
-### Windows Users
-
-If you are a Windows user who is new to Python and TensorFlow, follow [this
-guide](Installation-Windows.md) to set up your Python environment.
-
-### Mac and Unix Users
-
 [Download](https://www.python.org/downloads/) and install Python 3.6 if you do not
 already have it.
 
 `ml-agents/` subdirectory and run from the command line:
 
 ```sh
-pip3 install .
+pip3 install -e .
+
+**Notes:**
+
+- We do not currently support Python 3.7 or Python 3.5.
+- If you are using Anaconda and are having trouble with TensorFlow, please see
+  the following
+  [note](https://www.tensorflow.org/install/install_mac#installing_with_anaconda)
+  on how to install TensorFlow in an Anaconda environment.
 
 ## Docker-based Installation
 

docs/Migrating.md

   python3 learn.py 3DBall --train
   ```
 
-  from the `python/` directory, we now run
+  from the `python` subdirectory (which is changed to `ml-agents` subdirectory
+  in v0.5), we now run
-  from the directory where we installed the ML-Agents Toolkit.
+  from the root directory where we installed the ML-Agents Toolkit.
 
 * It is now required to specify the path to the yaml trainer configuration file
   when running `mlagents-learn`. For an example trainer configuration file, see
 ### Python API
 
 * We've changed some of the Python packages dependencies in requirement.txt
-  file. Make sure to run `pip3 install .` within your `ml-agents/python` folder
+  file. Make sure to run `pip3 install -e .` within your `ml-agents/python`
+  folder
   to update your Python packages.
 
 ## Migrating from ML-Agents toolkit v0.2 to v0.3

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

     ```sh
     git clone https://github.com/Unity-Technologies/ml-agents.git
     cd ml-agents/ml-agents/
-    pip3 install .
+    pip3 install -e .
     ```
 
 ### Setting up X Server (optional)