Unity-Technologies · awjuliani · Sep 5, 2018 · Aug 29, 2018 · Aug 29, 2018 · Aug 29, 2018
diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
@@ -67,7 +67,8 @@ members of the project's leadership.
 
 ## Attribution
 
-This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
-available at https://www.contributor-covenant.org/version/1/4/code-of-conduct/
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 1.4, available at
+https://www.contributor-covenant.org/version/1/4/code-of-conduct/
 
 [homepage]: https://www.contributor-covenant.org
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -1,54 +1,56 @@
 # Contribution Guidelines
 
-Thank you for your interest in contributing to the ML-Agents toolkit! We are incredibly
-excited to see how members of our community will use and extend the ML-Agents toolkit.
-To facilitate your contributions, we've outlined a brief set of guidelines
-to ensure that your extensions can be easily integrated.
+Thank you for your interest in contributing to the ML-Agents toolkit! We are
+incredibly excited to see how members of our community will use and extend the
+ML-Agents toolkit. To facilitate your contributions, we've outlined a brief set
+of guidelines to ensure that your extensions can be easily integrated.
 
-### Communication
+## Communication
 
-First, please read through our [code of conduct](CODE_OF_CONDUCT.md), 
-as we expect all our contributors to follow it.
+First, please read through our [code of conduct](CODE_OF_CONDUCT.md), as we
+expect all our contributors to follow it.
 
-Second, before starting on a project that you intend to contribute
-to the ML-Agents toolkit (whether environments or modifications to the codebase), 
-we **strongly** recommend posting on our 
-[Issues page](https://github.com/Unity-Technologies/ml-agents/issues) and
-briefly outlining the changes you plan to make. This will enable us to provide
-some context that may be helpful for you. This could range from advice and 
-feedback on how to optimally perform your changes or reasons for not doing it.
+Second, before starting on a project that you intend to contribute to the
+ML-Agents toolkit (whether environments or modifications to the codebase), we
+**strongly** recommend posting on our
+[Issues page](https://github.com/Unity-Technologies/ml-agents/issues)
+and briefly outlining the changes you plan to make. This will enable us to
+provide some context that may be helpful for you. This could range from advice
+and feedback on how to optimally perform your changes or reasons for not doing
+it.
 
 Lastly, if you're looking for input on what to contribute, feel free to
 reach out to us directly at ml-agents@unity3d.com and/or browse the GitHub
 issues with the `contributions welcome` label.
 
-### Git Branches
+## Git Branches
 
-Starting with v0.3, we adopted the 
+Starting with v0.3, we adopted the
 [Gitflow Workflow](http://nvie.com/posts/a-successful-git-branching-model/).
-Consequently, the `master` branch corresponds to the latest release of 
+Consequently, the `master` branch corresponds to the latest release of
 the project, while the `develop` branch corresponds to the most recent, stable,
 version of the project.
 
 Thus, when adding to the project, **please branch off `develop`**
 and make sure that your Pull Request (PR) contains the following:
+
 * Detailed description of the changes performed
-* Corresponding changes to documentation, unit tests and sample environments 
-(if applicable)
+* Corresponding changes to documentation, unit tests and sample environments (if
+  applicable)
 * Summary of the tests performed to validate your changes
 * Issue numbers that the PR resolves (if any)
 
-### Environments
+## Environments
 
-We are also actively open to adding community contributed environments as 
-examples, as long as they are small, simple, demonstrate a unique feature of 
-the platform, and provide a unique non-trivial challenge to modern 
+We are also actively open to adding community contributed environments as
+examples, as long as they are small, simple, demonstrate a unique feature of
+the platform, and provide a unique non-trivial challenge to modern
 machine learning algorithms. Feel free to submit these environments with a
-PR explaining the nature of the environment and task. 
+PR explaining the nature of the environment and task.
 
-### Style Guide
+## Style Guide
 
-When performing changes to the codebase, ensure that you follow the style
-guide of the file you're modifying. For Python, we follow 
-[PEP 8](https://www.python.org/dev/peps/pep-0008/). For C#, we will soon be
-adding a formal style guide for our repository.
+When performing changes to the codebase, ensure that you follow the style guide
+of the file you're modifying. For Python, we follow
+[PEP 8](https://www.python.org/dev/peps/pep-0008/).
+For C#, we will soon be adding a formal style guide for our repository.
diff --git a/MLAgentsSDK/README.md b/MLAgentsSDK/README.md
@@ -0,0 +1 @@
+# ML-Agents SDK
diff --git a/README.md b/README.md
@@ -4,75 +4,81 @@
 
 # Unity ML-Agents Toolkit (Beta)
 
-**The Unity Machine Learning Agents Toolkit** (ML-Agents) is an open-source Unity plugin 
-that enables games and simulations to serve as environments for training
-intelligent agents. Agents can be trained using reinforcement learning,
-imitation learning, neuroevolution, or other machine learning methods through
-a simple-to-use Python API. We also provide implementations (based on
-TensorFlow) of state-of-the-art algorithms to enable game developers
-and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games.
-These trained agents can be used for multiple purposes, including
-controlling NPC behavior (in a variety of settings such as multi-agent and
-adversarial), automated testing of game builds and evaluating different game
-design decisions pre-release. The ML-Agents toolkit is mutually beneficial for both game
-developers and AI researchers as it provides a central platform where advances
-in AI can be evaluated on Unity’s rich environments and then made accessible
-to the wider research and game developer communities. 
+**The Unity Machine Learning Agents Toolkit** (ML-Agents) is an open-source
+Unity plugin that enables games and simulations to serve as environments for
+training intelligent agents. Agents can be trained using reinforcement learning,
+imitation learning, neuroevolution, or other machine learning methods through a
+simple-to-use Python API. We also provide implementations (based on TensorFlow)
+of state-of-the-art algorithms to enable game developers and hobbyists to easily
+train intelligent agents for 2D, 3D and VR/AR games. These trained agents can be
+used for multiple purposes, including controlling NPC behavior (in a variety of
+settings such as multi-agent and adversarial), automated testing of game builds
+and evaluating different game design decisions pre-release. The ML-Agents
+toolkit is mutually beneficial for both game developers and AI researchers as it
+provides a central platform where advances in AI can be evaluated on Unity’s
+rich environments and then made accessible to the wider research and game
+developer communities.
 
 ## Features
+
 * Unity environment control from Python
 * 10+ sample Unity environments
 * Support for multiple environment configurations and training scenarios
-* Train memory-enhanced Agents using deep reinforcement learning
+* Train memory-enhanced agents using deep reinforcement learning
 * Easily definable Curriculum Learning scenarios
-* Broadcasting of Agent behavior for supervised learning
+* Broadcasting of agent behavior for supervised learning
 * Built-in support for Imitation Learning
-* Flexible Agent control with On Demand Decision Making
+* Flexible agent control with On Demand Decision Making
 * Visualizing network outputs within the environment
 * Simplified set-up with Docker
 
 ## Documentation
 
-* For more information, in addition to installation and usage
-instructions, see our [documentation home](docs/Readme.md).
-* If you have
-used a version of the ML-Agents toolkit prior to v0.4, we strongly recommend 
-our [guide on migrating from earlier versions](docs/Migrating.md).
+* For more information, in addition to installation and usage instructions, see
+  our [documentation home](docs/Readme.md).
+* If you have used a version of the ML-Agents toolkit prior to v0.4, we strongly
+  recommend our [guide on migrating from earlier versions](docs/Migrating.md).
 
 ## References
 
 We have published a series of blog posts that are relevant for ML-Agents:
-- Overviewing reinforcement learning concepts
-([multi-armed bandit](https://blogs.unity3d.com/2017/06/26/unity-ai-themed-blog-entries/)
-and [Q-learning](https://blogs.unity3d.com/2017/08/22/unity-ai-reinforcement-learning-with-q-learning/))
-- [Using Machine Learning Agents in a real game: a beginner’s guide](https://blogs.unity3d.com/2017/12/11/using-machine-learning-agents-in-a-real-game-a-beginners-guide/)
-- [Post](https://blogs.unity3d.com/2018/02/28/introducing-the-winners-of-the-first-ml-agents-challenge/) announcing the winners of our
-[first ML-Agents Challenge](https://connect.unity.com/challenges/ml-agents-1)
-- [Post](https://blogs.unity3d.com/2018/01/23/designing-safer-cities-through-simulations/)
-overviewing how Unity can be leveraged as a simulator to design safer cities.
+
+* Overviewing reinforcement learning concepts
+  ([multi-armed bandit](https://blogs.unity3d.com/2017/06/26/unity-ai-themed-blog-entries/)
+  and
+  [Q-learning](https://blogs.unity3d.com/2017/08/22/unity-ai-reinforcement-learning-with-q-learning/))
+* [Using Machine Learning Agents in a real game: a beginner’s guide](https://blogs.unity3d.com/2017/12/11/using-machine-learning-agents-in-a-real-game-a-beginners-guide/)
+* [Post](https://blogs.unity3d.com/2018/02/28/introducing-the-winners-of-the-first-ml-agents-challenge/)
+  announcing the winners of our
+  [first ML-Agents Challenge](https://connect.unity.com/challenges/ml-agents-1)
+* [Post](https://blogs.unity3d.com/2018/01/23/designing-safer-cities-through-simulations/)
+  overviewing how Unity can be leveraged as a simulator to design safer cities.
 
 In addition to our own documentation, here are some additional, relevant articles:
-- [Unity AI - Unity 3D Artificial Intelligence](https://www.youtube.com/watch?v=bqsfkGbBU6k)
-- [A Game Developer Learns Machine Learning](https://mikecann.co.uk/machine-learning/a-game-developer-learns-machine-learning-intent/)
-- [Explore Unity Technologies ML-Agents Exclusively on Intel Architecture](https://software.intel.com/en-us/articles/explore-unity-technologies-ml-agents-exclusively-on-intel-architecture)
+
+* [Unity AI - Unity 3D Artificial Intelligence](https://www.youtube.com/watch?v=bqsfkGbBU6k)
+* [A Game Developer Learns Machine Learning](https://mikecann.co.uk/machine-learning/a-game-developer-learns-machine-learning-intent/)
+* [Explore Unity Technologies ML-Agents Exclusively on Intel Architecture](https://software.intel.com/en-us/articles/explore-unity-technologies-ml-agents-exclusively-on-intel-architecture)
 
 ## Community and Feedback
 
-The ML-Agents toolkit is an open-source project and we encourage and welcome contributions.
-If you wish to contribute, be sure to review our 
-[contribution guidelines](CONTRIBUTING.md) and 
+The ML-Agents toolkit is an open-source project and we encourage and welcome
+contributions. If you wish to contribute, be sure to review our
+[contribution guidelines](CONTRIBUTING.md) and
 [code of conduct](CODE_OF_CONDUCT.md).
 
 You can connect with us and the broader community
 through Unity Connect and GitHub:
+
 * Join our
-[Unity Machine Learning Channel](https://connect.unity.com/messages/c/035fba4f88400000)
-to connect with others using the ML-Agents toolkit and Unity developers enthusiastic
-about machine learning. We use that channel to surface updates
-regarding the ML-Agents toolkit (and, more broadly, machine learning in games).
-* If you run into any problems using the ML-Agents toolkit, 
-[submit an issue](https://github.com/Unity-Technologies/ml-agents/issues) and
-make sure to include as much detail as possible.
+  [Unity Machine Learning Channel](https://connect.unity.com/messages/c/035fba4f88400000)
+  to connect with others using the ML-Agents toolkit and Unity developers
+  enthusiastic about machine learning. We use that channel to surface updates
+  regarding the ML-Agents toolkit (and, more broadly, machine learning in
+  games).
+* If you run into any problems using the ML-Agents toolkit,
+  [submit an issue](https://github.com/Unity-Technologies/ml-agents/issues) and
+  make sure to include as much detail as possible.
 
 For any other questions or feedback, connect directly with the ML-Agents
 team at ml-agents@unity3d.com.
@@ -86,7 +92,7 @@ of the documentation to one language (Chinese), but we hope to continue
 translating more pages and to other languages. Consequently,
 we welcome any enhancements and improvements from the community.
 
-- [Chinese](docs/localized/zh-CN/)
+* [Chinese](docs/localized/zh-CN/)
 
 ## License
 

diff --git a/docs/API-Reference.md b/docs/API-Reference.md
@@ -1,7 +1,7 @@
 # API Reference
 
 Our developer-facing C# classes (Academy, Agent, Decision and Monitor) have been
-documented to be compatabile with
+documented to be compatible with
 [Doxygen](http://www.stack.nl/~dimitri/doxygen/) for auto-generating HTML
 documentation.
 

diff --git a/docs/Background-TensorFlow.md b/docs/Background-TensorFlow.md
@@ -16,7 +16,7 @@ to TensorFlow-related tools that we leverage within the ML-Agents toolkit.
 performing computations using data flow graphs, the underlying representation of
 deep learning models. It facilitates training and inference on CPUs and GPUs in
 a desktop, server, or mobile device. Within the ML-Agents toolkit, when you
-train the behavior of an Agent, the output is a TensorFlow model (.bytes) file
+train the behavior of an agent, the output is a TensorFlow model (.bytes) file
 that you can then embed within an Internal Brain. Unless you implement a new
 algorithm, the use of TensorFlow is mostly abstracted away and behind the
 scenes.

diff --git a/docs/Basic-Guide.md b/docs/Basic-Guide.md
@@ -1,6 +1,6 @@
 # Basic Guide
 
-This guide will show you how to use a pretrained model in an example Unity
+This guide will show you how to use a pre-trained model in an example Unity
 environment, and show you how to train the model yourself.
 
 If you are not familiar with the [Unity Engine](https://unity3d.com/unity), we
@@ -13,7 +13,7 @@ the basic concepts of Unity.
 In order to use the ML-Agents toolkit within Unity, you need to change some
 Unity settings first. Also [TensorFlowSharp
 plugin](https://s3.amazonaws.com/unity-ml-agents/0.4/TFSharpPlugin.unitypackage)
-is needed for you to use pretrained model within Unity, which is based on the
+is needed for you to use pre-trained model within Unity, which is based on the
 [TensorFlowSharp repo](https://github.com/migueldeicaza/TensorFlowSharp).
 
 1. Launch Unity
@@ -70,14 +70,14 @@ if you want to [use an executable](Learning-Environment-Executable.md) or to
 `None` if you want to interact with the current scene in the Unity Editor.
 
 More information and documentation is provided in the
-[Python API](../ml-agents/README.md) page.
+[Python API](Python-API.md) page.
 
 ## Training the Brain with Reinforcement Learning
 
 ### Setting the Brain to External
 
 Since we are going to build this environment to conduct training, we need to set
-the brain used by the agents to **External**. This allows the agents to
+the Brain used by the Agents to **External**. This allows the Agents to
 communicate with the external training process when making their decisions.
 
 1. In the **Scene** window, click the triangle icon next to the Ball3DAcademy
@@ -90,17 +90,23 @@ communicate with the external training process when making their decisions.
 ### Training the environment
 
 1. Open a command or terminal window.
-2. Nagivate to the folder where you installed the ML-Agents toolkit.
+2. Navigate to the folder where you cloned the ML-Agents toolkit repository.
+   **Note**: If you followed the default [installation](Installation.md), then
+   you should be able to run `mlagents-learn` from any directory.
 3. Run `mlagents-learn <trainer-config-path> --run-id=<run-identifier> --train`
-   Where:
+   where:
     - `<trainer-config-path>` is the relative or absolute filepath of the
-      trainer configuration. The defaults used by environments in the ML-Agents
-      SDK can be found in `config/trainer_config.yaml`.
+      trainer configuration. The defaults used by example environments included
+      in `MLAgentsSDK` can be found in `config/trainer_config.yaml`.
     - `<run-identifier>` is a string used to separate the results of different
       training runs
-    - And the `--train` tells `mlagents-learn` to run a training session (rather
+    - `--train` tells `mlagents-learn` to run a training session (rather
       than inference)
-4. When the message _"Start training by pressing the Play button in the Unity
+4. If you cloned the ML-Agents repo, then you can simply run
+      ```sh
+      mlagents-learn config/trainer_config.yaml --run-id=firstRun --train
+      ```
+5. When the message _"Start training by pressing the Play button in the Unity
    Editor"_ is displayed on the screen, you can press the :arrow_forward: button
    in Unity to start training in the Editor.
 
@@ -143,6 +149,7 @@ INFO:mlagents.learn:{'--curriculum': 'None',
  '--train': True,
  '--worker-id': '0',
  '<trainer-config-path>': 'config/trainer_config.yaml'}
+INFO:mlagents.envs:Start training by pressing the Play button in the Unity Editor.
  ```
 
 **Note**: If you're using Anaconda, don't forget to activate the ml-agents
@@ -152,7 +159,6 @@ If `mlagents-learn` runs correctly and starts training, you should see something
 like this:
 
 ```console
-INFO:mlagents.envs:Start training by pressing the Play button in the Unity Editor.
 INFO:mlagents.envs:
 'Ball3DAcademy' started successfully!
 Unity Academy name: Ball3DAcademy
@@ -208,7 +214,7 @@ You can press Ctrl+C to stop the training, and your trained model will be at
 `models/<run-identifier>/editor_<academy_name>_<run-identifier>.bytes` where
 `<academy_name>` is the name of the Academy GameObject in the current scene.
 This file corresponds to your model's latest checkpoint. You can now embed this
-trained model into your internal brain by following the steps below, which is
+trained model into your Internal Brain by following the steps below, which is
 similar to the steps described
 [above](#play-an-example-environment-using-pretrained-model).
 
@@ -229,7 +235,7 @@ similar to the steps described
   page.
 - For a more detailed walk-through of our 3D Balance Ball environment, check out
   the [Getting Started](Getting-Started-with-Balance-Ball.md) page.
-- For a "Hello World" introduction to creating your own learning environment,
+- 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