Fixing Typos

docs/Background-TensorFlow.md

 hyperparameters and setting the optimal values for your Unity environment. We
 provide more details on setting the hyperparameters in later parts of the
 documentation, but, in the meantime, if you are unfamiliar with TensorBoard we
-recommend our guide on [using Tensorboard with ML-Agents](Using-Tensorboard.md) or
+recommend our guide on [using TensorBoard with ML-Agents](Using-Tensorboard.md) or
 this [tutorial](https://github.com/dandelionmane/tf-dev-summit-tensorboard-tutorial).
 

docs/Basic-Guide.md

 8. Select the **InferenceDevice** to use for this model (CPU or GPU). 
    _Note: CPU is faster for the majority of ML-Agents toolkit generated models_
 9. Click the **Play** button and you will see the platforms balance the balls
-   using the pretrained model.
+   using the pre-trained model.
-   ![Running a pretrained model](images/running-a-pretrained-model.gif)
+   ![Running a pre-trained model](images/running-a-pretrained-model.gif)
-contains a simple walkthrough of the functionality of the Python API. It can
+contains a simple walk-through of the functionality of the Python API. It can
 also serve as a simple test that your environment is configured correctly.
 Within `Basics`, be sure to set `env_name` to the name of the Unity executable
 if you want to [use an executable](Learning-Environment-Executable.md) or to
 corresponds to your model's latest checkpoint. You can now embed this trained
 model into your Learning Brain by following the steps below, which is similar to
 the steps described
-[above](#play-an-example-environment-using-pretrained-model).
+[above](#running-a-pre-trained-model).
 
 1. Move your model file into
    `UnitySDK/Assets/ML-Agents/Examples/3DBall/TFModels/`.
 - For a "Hello World" introduction to creating your own Learning Environment,
   check out the [Making a New Learning
   Environment](Learning-Environment-Create-New.md) page.
-- For a series of Youtube video tutorials, checkout the
+- For a series of YouTube video tutorials, checkout the
   [Machine Learning Agents PlayList](https://www.youtube.com/playlist?list=PLX2vGYjWbI0R08eWQkO7nQkGiicHAX7IX)
   page.

docs/Installation-Windows.md

 Next, install `tensorflow-gpu` using `pip`. You'll need version 1.7.1. In an
 Anaconda Prompt with the Conda environment ml-agents activated, type in the
 following command to uninstall TensorFlow for cpu and install TensorFlow
-for gpu _(make sure you are connected to the internet)_:
+for gpu _(make sure you are connected to the Internet)_:
 
 ```sh
 pip uninstall tensorflow
 Found device 0 with properties ...
 ```
 
-## Acknowledgements
+## Acknowledgments
 
 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/)

docs/Learning-Environment-Design-Academy.md

 
 ## Initializing an Academy
 
-Initialization is performed once in an Academy object's lifecycle. Use the
+Initialization is performed once in an Academy object's life cycle. Use the
 `InitializeAcademy()` method for any logic you would normally perform in the
 standard Unity `Start()` or `Awake()` methods.
 

docs/Learning-Environment-Design-Learning-Brains.md

 
 The **Learning Brain** works differently if you are training it or not.
 When training your Agents, drag the **Learning Brain** to the
-Academy's `Broadcast Hub` and check the checkbox `Control`. When using a pretrained 
+Academy's `Broadcast Hub` and check the checkbox `Control`. When using a pre-trained 
 model, just drag the Model file into the `Model` property of the **Learning Brain**.
 
 ## Training Mode / External Control

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

 This page contains instructions for setting up an EC2 instance on Amazon Web
 Service for training ML-Agents environments.
 
-## Preconfigured AMI
+## Pre-configured AMI
-We've prepared a preconfigured AMI for you with the ID: `ami-016ff5559334f8619` in the
+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 

gym-unity/README.md

 
 ## Using the Gym Wrapper
 
-The gym interface is available from `gym_unity.envs`. To launch an environmnent
+The gym interface is available from `gym_unity.envs`. To launch an environment
 from the root of the project repository use:
 
 ```python