changes reflecting comments on github

docs/Readme.md

 * [Installation](Installation.md)
   * [Background: Jupyter Notebooks](Background-Jupyter.md)
   * [Docker Set-up](Using-Docker.md)
-  * [Using Python Virtual Environment](Python-venv.md) 
-
+  * [Using Python Virtual Environment](Using-Virtual-Environment.md) 
+* [Basic Guide](Basic-Guide.md)
-* [Basic Guide](Basic-Guide.md)
 * [ML-Agents Toolkit Overview](ML-Agents-Overview.md)
   * [Background: Unity](Background-Unity.md)
   * [Background: Machine Learning](Background-Machine-Learning.md)
     [Learning](Learning-Environment-Design-Learning-Brains.md)
 * [Learning Environment Best Practices](Learning-Environment-Best-Practices.md)
 
-Optional for first time users
+### Advanced Usage
   * [Using the Monitor](Feature-Monitor.md)
   * [Using the Video Recorder](https://github.com/Unity-Technologies/video-recorder)
   * [Using an Executable Environment](Learning-Environment-Executable.md)
 * [Training ML-Agents](Training-ML-Agents.md)
 * [Using TensorBoard to Observe Training](Using-Tensorboard.md)
 * [Training with Proximal Policy Optimization](Training-PPO.md)
+* [Training with Soft Actor-Critic](Training-SAC.md)
-* [Training with Soft Actor-Critic](Training-SAC.md)
 * [Training with Curriculum Learning](Training-Curriculum-Learning.md)
 * [Training with Imitation Learning](Training-Imitation-Learning.md)
 * [Training with LSTM](Feature-Memory.md)

docs/Training-ML-Agents.md

 
 And then opening the URL: [localhost:6006](http://localhost:6006).
 
+**Note:** The default port TensorBoard uses is 6006. If there is an existing session
+running on port 6006 a new session can be launched on an open port using the --port 
+option.
+
 When training is finished, you can find the saved model in the `models` folder
 under the assigned run-id — in the cats example, the path to the model would be
 `models/cob_1/CatsOnBicycles_cob_1.nn`.
 * `--num-envs=<n>`: Specifies the number of concurrent Unity environment instances to 
   collect experiences from when training. Defaults to 1.
 * `--base-port`: Specifies the starting port. Each concurrent Unity environment instance 
-  will get assigned a port sequentially, starting from the `base-port`.  Each instance 
+  will get assigned a port sequentially, starting from the `base-port`. Each instance 
   will use the port `(base_port + worker_id)`, where the `worker_id` is sequential IDs 
   given to each instance from 0 to `num_envs - 1`. Default is 5005.
 * `--docker-target-name=<dt>`: The Docker Volume on which to store curriculum,

docs/Using-Tensorboard.md

 
 4. Open a browser window and navigate to [localhost:6006](http://localhost:6006).
 
-**Note:** The default port tensorboard uses is 6006. If there is an existing session
+**Note:** The default port TensorBoard uses is 6006. If there is an existing session
 running on port 6006 a new session can be launched on an open port using the --port 
 option.
 

docs/Using-Virtual-Environment.md

+# Using Virtual Environment
+
+## What is a Virtual Environment?
+A Python Virtual Environment is an isolated workspace for Python projects. 
+
+## Why should I use a Virtual Environment?
+A Virtual Environment keeps all dependencies for the project separate from dependencies 
+of other projects. This has two advantages:
+1. Firstly, it makes dependency management for the project easy.
+1. Secondly, it enables using and testing of different library versions by quickly 
+spinning up a new environment and verifying the compatibility of the code with the
+different version. 
+
+Requirement - Python 3.6 must be installed on the installed on the machine you would like 
+to run ML-Agents on (either local laptop/desktop or remote server). Python 3.6 can be 
+installed from [here](https://www.python.org/downloads/). 
+
+## Mac OS X Setup
+
+1. Create a folder where the virtual environments will live ` $ mkdir ~/python-venvs `
+1. To create a new environment named `test-env` execute `$ python3 -m venv ~/python-envs/test-env`  
+1. To activate the environment execute `$ source ~/python-envs/test-env/bin/activate`
+1. Install ML-Agents package using `$ pip3 install mlagents`
+1. To deactivate the environment execute `$ deactivate `
+
+## Ubuntu Setup 
+
+1. Install the python3-venv package using `$ sudo apt-get install python3-venv`
+1. Now follow the steps in the Mac OS X installation.
+
+## Windows Setup
+
+Coming Soon.

docs/Python-venv.md

-# Installing and Running ML-Agents in a virtual environment
-
-__Requirement - Python 3.6 must be installed on the server. Python 3.6 can be [here](https://www.python.org/downloads/)__ 
-
-## Mac OS X Setup
-
-1. Create a folder where the virtual environments will live ` $ mkdir ~/python-venvs `
-1. To create a new environment named `test-env` execute `$ python3 -m venv ~/python-envs/test-env`  
-1. To activate the environment execute `$ source ~/python-envs/test-env/bin/activate`
-1. Install ML-Agents package using `$ pip3 install mlagents`
-1. To deactivate the environment execute `$ deactivate `
-
-## Ubuntu Setup 
-
-1. Install the python3-venv package using `$ sudo apt-get install python3-venv`
-
-Now follow the steps in the Mac OS X installation.