Merge pull request #916 from Unity-Technologies/hotfix-trademarkupdate

update for trademark and consistency of ml-agents

Author: awjuliani

CONTRIBUTING.md

@@ -1,7 +1,7 @@
 # Contribution Guidelines
 
-Thank you for your interest in contributing to ML-Agents! We are incredibly
-excited to see how members of our community will use and extend ML-Agents.
+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.
 
@@ -11,7 +11,7 @@ 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 ML-Agents (whether environments or modifications to the codebase), 
+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

README.md

@@ -1,8 +1,8 @@
 <img src="docs/images/unity-wide.png" align="middle" width="3000"/>
 
-# Unity ML-Agents (Beta)
+# Unity ML-Agents Toolkit (Beta)
 
-**Unity Machine Learning Agents** (ML-Agents) is an open-source Unity plugin 
+**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
@@ -12,7 +12,7 @@ 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. ML-Agents is mutually beneficial for both 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. 
@@ -34,7 +34,7 @@ to the wider research and game developer communities.
 * For more information, in addition to installation and usage
 instructions, see our [documentation home](docs/Readme.md).
 * If you have
-used a version of ML-Agents prior to v0.4, we strongly recommend 
+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
@@ -56,7 +56,7 @@ In addition to our own documentation, here are some additional, relevant article
 
 ## Community and Feedback
 
-ML-Agents is an open-source project and we encourage and welcome contributions.
+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).
@@ -65,10 +65,10 @@ 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 ML-Agents and Unity developers enthusiastic
+to connect with others using the ML-Agents toolkit and Unity developers enthusiastic
 about machine learning. We use that channel to surface updates
-regarding ML-Agents (and, more broadly, machine learning in games).
-* If you run into any problems using ML-Agents, 
+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.
 
@@ -77,7 +77,7 @@ team at [email protected].
 
 ## Translations
 
-To make Unity ML-Agents accessible to the global research and
+To make the Unity ML-Agents toolkit accessible to the global research and
 Unity developer communities, we're attempting to create and maintain
 translations of our documentation. We've started with translating a subset
 of the documentation to one language (Chinese), but we hope to continue

docs/API-Reference.md

@@ -11,7 +11,7 @@ the following command within the `docs/` directory:
 
     doxygen dox-ml-agents.conf
 
-`dox-ml-agents.conf` is a Doxygen configuration file for ML-Agents
+`dox-ml-agents.conf` is a Doxygen configuration file for the ML-Agents toolkit
 that includes the classes that have been properly formatted. 
 The generated HTML files will be placed
 in the `html/` subdirectory. Open `index.html` within that subdirectory to 

docs/Background-Machine-Learning.md

@@ -1,8 +1,8 @@
 # Background: Machine Learning
 
-Given that a number of users of ML-Agents might not have a formal machine 
+Given that a number of users of the ML-Agents toolkit might not have a formal machine 
 learning background, this page provides an overview to facilitate the 
-understanding of ML-Agents. However, We will not attempt to provide a thorough 
+understanding of the ML-Agents toolkit. However, We will not attempt to provide a thorough 
 treatment of machine learning as there are fantastic resources online.
 
 Machine learning, a branch of artificial intelligence, focuses on learning 
@@ -77,7 +77,7 @@ tasks are active areas of machine learning research and, in practice, require
 several iterations to achieve good performance.
 
 We now switch to reinforcement learning, the third class of
-machine learning algorithms, and arguably the one most relevant for ML-Agents.
+machine learning algorithms, and arguably the one most relevant for the ML-Agents toolkit.
 
 ## Reinforcement Learning
 
@@ -132,8 +132,8 @@ in many ways, one can view a non-playable character (NPC) as a virtual
 robot, with its own observations about the environment, its own set of actions
 and a specific objective. Thus it is natural to explore how we can
 train behaviors within Unity using reinforcement learning. This is precisely
-what ML-Agents offers. The video linked below includes a reinforcement
-learning demo showcasing training character behaviors using ML-Agents.
+what the ML-Agents toolkit offers. The video linked below includes a reinforcement
+learning demo showcasing training character behaviors using the ML-Agents toolkit.
 
 <p align="center">
     <a href="http://www.youtube.com/watch?feature=player_embedded&v=fiQsmdwEGT8" target="_blank">

docs/Background-TensorFlow.md

@@ -2,19 +2,19 @@
 
 As discussed in our 
 [machine learning background page](Background-Machine-Learning.md), many of the
-algorithms we provide in ML-Agents leverage some form of deep learning.
+algorithms we provide in the ML-Agents toolkit leverage some form of deep learning.
 More specifically, our implementations are built on top of the open-source 
 library [TensorFlow](https://www.tensorflow.org/). This means that the models
-produced by ML-Agents are (currently) in a format only understood by
+produced by the ML-Agents toolkit are (currently) in a format only understood by
 TensorFlow. In this page we provide a brief overview of TensorFlow, in addition
-to TensorFlow-related tools that we leverage within ML-Agents.
+to TensorFlow-related tools that we leverage within the ML-Agents toolkit.
 
 ## TensorFlow
 
 [TensorFlow](https://www.tensorflow.org/) is an open source library for
 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 ML-Agents, when you
+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 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 
@@ -47,5 +47,5 @@ that contains an Internal Brain is built, inference is performed via
 TensorFlowSharp. We provide an additional in-depth overview of how to
 leverage [TensorFlowSharp within Unity](Using-TensorFlow-Sharp-in-Unity.md)
 which will become more relevant once you install and start training
-behaviors within ML-Agents. Given the reliance on TensorFlowSharp, the
+behaviors within the ML-Agents toolkit. Given the reliance on TensorFlowSharp, the
 Internal Brain is currently marked as experimental.

docs/Background-Unity.md

@@ -6,7 +6,7 @@ we highly recommend the
 [Tutorials page](https://unity3d.com/learn/tutorials). The 
 [Roll-a-ball tutorial](https://unity3d.com/learn/tutorials/s/roll-ball-tutorial)
 is a fantastic resource to learn all the basic concepts of Unity to get started
-with ML-Agents: 
+with the ML-Agents toolkit: 
 * [Editor](https://docs.unity3d.com/Manual/UsingTheEditor.html)
 * [Interface](https://docs.unity3d.com/Manual/LearningtheInterface.html)
 * [Scene](https://docs.unity3d.com/Manual/CreatingScenes.html)

docs/Basic-Guide.md

@@ -5,13 +5,13 @@ This guide will show you how to use a pretrained model in an example Unity envir
 If you are not familiar with the [Unity Engine](https://unity3d.com/unity),
 we highly recommend the [Roll-a-ball tutorial](https://unity3d.com/learn/tutorials/s/roll-ball-tutorial) to learn all the basic concepts of Unity. 
 
-## Setting up ML-Agents within Unity
+## Setting up the ML-Agents Toolkit within Unity
 
-In order to use ML-Agents 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 [TensorFlowSharp repo](https://github.com/migueldeicaza/TensorFlowSharp). 
+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 [TensorFlowSharp repo](https://github.com/migueldeicaza/TensorFlowSharp). 
 
 1. Launch Unity
 2. On the Projects dialog, choose the **Open** option at the top of the window.
-3. Using the file dialog that opens, locate the `unity-environment` folder within the ML-Agents project and click **Open**.
+3. Using the file dialog that opens, locate the `unity-environment` folder within the the ML-Agents toolkit project and click **Open**.
 4. Go to **Edit** > **Project Settings** > **Player**
 5. For **each** of the platforms you target 
 (**PC, Mac and Linux Standalone**, **iOS** or **Android**):
@@ -67,7 +67,7 @@ object.
 
 ### Training the environment
 1. Open a command or terminal window. 
-2. Nagivate to the folder where you installed ML-Agents. 
+2. Nagivate to the folder where you installed the ML-Agents toolkit. 
 3. Change to the `python` directory. 
 4. Run `python3 learn.py --run-id=<run-identifier> --train`
 Where:
@@ -99,7 +99,7 @@ to the **Graph Model** placeholder in the **Ball3DBrain** inspector window.
 
 ## Next Steps
 
-* For more information on ML-Agents, in addition to helpful background, check out the [ML-Agents Overview](ML-Agents-Overview.md) page.
+* For more information on the ML-Agents toolkit, in addition to helpful background, check out the [ML-Agents Toolkit Overview](ML-Agents-Overview.md) 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, check out the [Making a New Learning Environment](Learning-Environment-Create-New.md) page.
 * For a series of Youtube video tutorials, checkout the [Machine Learning Agents PlayList](https://www.youtube.com/playlist?list=PLX2vGYjWbI0R08eWQkO7nQkGiicHAX7IX) page. 

docs/FAQ.md

@@ -9,7 +9,7 @@ If you haven't switched your scripting runtime version from .NET 3.5 to .NET 4.6
 error CS1061: Type `System.Text.StringBuilder' does not contain a definition for `Clear' and no extension method `Clear' of type `System.Text.StringBuilder' could be found. Are you missing an assembly reference?
 ```
 
-This is because .NET 3.5 doesn't support method Clear() for StringBuilder, refer to [Setting Up ML-Agents Within Unity](Installation.md#setting-up-ml-agent-within-unity) for solution. 
+This is because .NET 3.5 doesn't support method Clear() for StringBuilder, refer to [Setting Up The ML-Agents Toolkit Within Unity](Installation.md#setting-up-ml-agent-within-unity) for solution. 
 
 ### TensorFlowSharp flag not turned on. 
 
@@ -19,7 +19,7 @@ If you have already imported the TensorFlowSharp plugin, but havn't set ENABLE_T
 You need to install and enable the TensorFlowSharp plugin in order to use the internal brain. 
 ```
 
-This error message occurs because the TensorFlowSharp plugin won't be usage without the ENABLE_TENSORFLOW flag, refer to [Setting Up ML-Agents Within Unity](Installation.md#setting-up-ml-agent-within-unity) for solution. 
+This error message occurs because the TensorFlowSharp plugin won't be usage without the ENABLE_TENSORFLOW flag, refer to [Setting Up The ML-Agents Toolkit Within Unity](Installation.md#setting-up-ml-agent-within-unity) for solution. 
 
 ### Tensorflow epsilon placeholder error
 

docs/Getting-Started-with-Balance-Ball.md

@@ -1,11 +1,11 @@
 # Getting Started with the 3D Balance Ball Environment
 
-This tutorial walks through the end-to-end process of opening an ML-Agents 
+This tutorial walks through the end-to-end process of opening a ML-Agents toolkit
 example environment in Unity, building the Unity executable, training an agent 
 in it, and finally embedding the trained model into the Unity environment. 
 
-ML-Agents includes a number of [example environments](Learning-Environment-Examples.md) 
-which you can examine to help understand the different ways in which ML-Agents 
+The ML-Agents toolkit includes a number of [example environments](Learning-Environment-Examples.md) 
+which you can examine to help understand the different ways in which the ML-Agents toolkit 
 can be used. These environments can also serve as templates for new 
 environments or as ways to test new ML algorithms. After reading this tutorial, 
 you should be able to explore and build the example environments.
@@ -24,7 +24,7 @@ Let's get started!
 
 ## Installation
 
-In order to install and set up ML-Agents, the Python dependencies and Unity, 
+In order to install and set up the ML-Agents toolkit, the Python dependencies and Unity, 
 see the [installation instructions](Installation.md).
 
 ## Understanding a Unity Environment (3D Balance Ball)
@@ -108,7 +108,7 @@ when you embed the trained model in the Unity application, you will change the
 **Vector Observation Space**
 
 Before making a decision, an agent collects its observation about its state 
-in the world. ML-Agents classifies vector observations into two types:
+in the world. The ML-Agents toolkit classifies vector observations into two types:
 **Continuous** and **Discrete**. The **Continuous** vector observation space 
 collects observations in a vector of floating point numbers. The **Discrete** 
 vector observation space is an index into a table of states. Most of the example
@@ -124,7 +124,7 @@ values are defined in the agent's `CollectObservations()` function.)
 **Vector Action Space**
 
 An agent is given instructions from the brain in the form of *actions*. Like 
-states, ML-Agents classifies actions into two types: the **Continuous** 
+states, ML-Agents toolkit classifies actions into two types: the **Continuous** 
 vector action space is a vector of numbers that can vary continuously. What 
 each element of the vector means is defined by the agent logic (the PPO
 training process just learns what values are better given particular state 
@@ -193,7 +193,7 @@ In order to train an agent to correctly balance the ball, we will use a
 Reinforcement Learning algorithm called Proximal Policy Optimization (PPO). 
 This is a method that has been shown to be safe, efficient, and more general 
 purpose than many other RL algorithms, as such we have chosen it as the 
-example algorithm for use with ML-Agents. For more information on PPO, 
+example algorithm for use with ML-Agents toolkit. For more information on PPO, 
 OpenAI has a recent [blog post](https://blog.openai.com/openai-baselines-ppo/) 
 explaining it.
 
@@ -217,7 +217,7 @@ When the message _"Start training by pressing the Play button in the Unity Edito
 
 **Note**: If you're using Anaconda, don't forget to activate the ml-agents environment first.
 
-The `--train` flag tells ML-Agents to run in training mode. 
+The `--train` flag tells the ML-Agents toolkit to run in training mode. 
 
 **Note**: You can train using an executable rather than the Editor. To do so, follow the intructions in 
 [Using an Execuatble](Learning-Environment-Executable.md).
@@ -271,7 +271,7 @@ Because TensorFlowSharp support is still experimental, it is disabled by
 default. In order to enable it, you must follow these steps. Please note that 
 the `Internal` Brain mode will only be available once completing these steps.
 
-To set up the TensorFlowSharp Support, follow [Setting up ML-Agents within Unity](Basic-Guide.md#setting-up-ml-agents-within-unity) section.
+To set up the TensorFlowSharp Support, follow [Setting up ML-Agents Toolkit within Unity](Basic-Guide.md#setting-up-ml-agents-within-unity) section.
 of the Basic Guide page.
 
 ### Embedding the trained model into Unity

docs/Glossary.md

@@ -1,4 +1,4 @@
-# ML-Agents Glossary
+# ML-Agents Toolkit Glossary
 
  * **Academy** - Unity Component which controls timing, reset, and 
  training/inference settings of the environment.