find and replace for ML-Agents Toolkit (Unity-Technologies#3799)

* find and replace for ML-Agents Toolkit

* apply

* install ruby

* apt-get update

* Update config.yml

.circleci/config.yml

@@ -107,6 +107,9 @@ jobs:
       - run:
           name: Install Dependencies
           command: |
+            # Need ruby for search-and-replace
+            sudo apt-get update
+            sudo apt-get install ruby-full
             python3 -m venv venv
             . venv/bin/activate
             pip install --upgrade pip

.pre-commit-config.yaml

@@ -72,6 +72,13 @@ repos:
             )$
         args: [--score=n]
 
+- repo: https://github.com/mattlqx/pre-commit-search-and-replace
+  rev: v1.0.3
+  hooks:
+  - id: search-and-replace
+    types: [markdown]
+    exclude: ".*localized.*"
+
 # "Local" hooks, see https://pre-commit.com/#repository-local-hooks
 -   repo: local
     hooks:

.pre-commit-search-and-replace.yaml

@@ -0,0 +1,4 @@
+- description: Replace "ML agents toolkit", "ML-Agents toolkit" etc
+  search: /ML[ -]Agents toolkit/
+  replacement: ML-Agents Toolkit
+  insensitive: true

com.unity.ml-agents/CONTRIBUTING.md

@@ -1,8 +1,8 @@
 # Contribution Guidelines
 
-Thank you for your interest in contributing to the ML-Agents toolkit! We are
+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
+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
@@ -11,7 +11,7 @@ First, please read through our [code of conduct](https://github.com/Unity-Techno
 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
+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

docs/API-Reference.md

@@ -10,7 +10,7 @@ and run the following command within the `docs/` directory:
 doxygen dox-ml-agents.conf
 ```
 
-`dox-ml-agents.conf` is a Doxygen configuration file for the ML-Agents toolkit
+`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 navigate to the API reference home. Note that `html/` is already

docs/Learning-Environment-Design.md

@@ -9,7 +9,7 @@ state, then the agent receives no reward or a negative reward (punishment). As
 the agent learns during training, it optimizes its decision making so that it
 receives the maximum reward over time.
 
-The ML-Agents toolkit uses a reinforcement learning technique called
+The ML-Agents Toolkit uses a reinforcement learning technique called
 [Proximal Policy Optimization (PPO)](https://blog.openai.com/openai-baselines-ppo/).
 PPO uses a neural network to approximate the ideal function that maps an agent's
 observations to the best action an agent can take in a given state. The
@@ -59,7 +59,7 @@ information.
 
 ## Organizing the Unity Scene
 
-To train and use the ML-Agents toolkit in a Unity scene, the scene as many Agent subclasses as you need.
+To train and use the ML-Agents Toolkit in a Unity scene, the scene as many Agent subclasses as you need.
 Agent instances should be attached to the GameObject representing that Agent.
 
 ### Academy
@@ -125,7 +125,7 @@ about programming your own Agents.
 
 ## Environments
 
-An _environment_ in the ML-Agents toolkit can be any scene built in Unity. The
+An _environment_ in the ML-Agents Toolkit can be any scene built in Unity. The
 Unity scene provides the environment in which agents observe, act, and learn.
 How you set up the Unity scene to serve as a learning environment really depends
 on your goal. You may be trying to solve a specific reinforcement learning

docs/ML-Agents-Overview.md

@@ -10,14 +10,14 @@ 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
+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.
 
 Depending on your background (i.e. researcher, game developer, hobbyist), you
 may have very different questions on your mind at the moment. To make your
-transition to the ML-Agents toolkit easier, we provide several background pages
+transition to the ML-Agents Toolkit easier, we provide several background pages
 that include overviews and helpful resources on the [Unity
 Engine](Background-Unity.md), [machine learning](Background-Machine-Learning.md)
 and [TensorFlow](Background-TensorFlow.md). We **strongly** recommend browsing
@@ -26,7 +26,7 @@ machine learning concepts or have not previously heard of TensorFlow.
 
 The remainder of this page contains a deep dive into ML-Agents, its key
 components, different training modes and scenarios. By the end of it, you should
-have a good sense of _what_ the ML-Agents toolkit allows you to do. The
+have a good sense of _what_ the ML-Agents Toolkit allows you to do. The
 subsequent documentation pages provide examples of _how_ to use ML-Agents.
 
 ## Running Example: Training NPC Behaviors
@@ -104,14 +104,14 @@ the process of learning a policy through running simulations is called the
 **training phase**, while playing the game with an NPC that is using its learned
 policy is called the **inference phase**.
 
-The ML-Agents toolkit provides all the necessary tools for using Unity as the
+The ML-Agents Toolkit provides all the necessary tools for using Unity as the
 simulation engine for learning the policies of different objects in a Unity
-environment. In the next few sections, we discuss how the ML-Agents toolkit
+environment. In the next few sections, we discuss how the ML-Agents Toolkit
 achieves this and what features it provides.
 
 ## Key Components
 
-The ML-Agents toolkit is a Unity plugin that contains three high-level
+The ML-Agents Toolkit is a Unity plugin that contains three high-level
 components:
 
 - **Learning Environment** - which contains the Unity scene and all the game
@@ -157,9 +157,9 @@ medics (medics and drivers have different actions).
        border="10" />
 </p>
 
-_Example block diagram of ML-Agents toolkit for our sample game._
+_Example block diagram of ML-Agents Toolkit for our sample game._
 
-We have yet to discuss how the ML-Agents toolkit trains behaviors, and what role
+We have yet to discuss how the ML-Agents Toolkit trains behaviors, and what role
 the Python API and External Communicator play. Before we dive into those
 details, let's summarize the earlier components. Each character is attached to
 an Agent, and each Agent has a Behavior. The Behavior can be thought as a function
@@ -189,7 +189,7 @@ inference can proceed.
 
 ### Built-in Training and Inference
 
-As mentioned previously, the ML-Agents toolkit ships with several
+As mentioned previously, the ML-Agents Toolkit ships with several
 implementations of state-of-the-art algorithms for training intelligent agents.
 More specifically, during training, all the medics in the
 scene send their observations to the Python API through the External
@@ -217,7 +217,7 @@ tutorial covers this training mode with the **3D Balance Ball** sample environme
 
 In the previous mode, the Agents were used for training to generate
 a TensorFlow model that the Agents can later use. However,
-any user of the ML-Agents toolkit can leverage their own algorithms for
+any user of the ML-Agents Toolkit can leverage their own algorithms for
 training. In this case, the behaviors of all the Agents in the scene
 will be controlled within Python.
 You can even turn your environment into a [gym.](../gym-unity/README.md)
@@ -260,7 +260,7 @@ update the random policy to a more meaningful one that is successively improved
 as the environment gradually increases in complexity. In our example, we can
 imagine first training the medic when each team only contains one player, and
 then iteratively increasing the number of players (i.e. the environment
-complexity). The ML-Agents toolkit supports setting custom environment
+complexity). The ML-Agents Toolkit supports setting custom environment
 parameters within the Academy. This allows elements of the environment related
 to difficulty or complexity to be dynamically adjusted based on training
 progress.
@@ -330,7 +330,7 @@ inspiration:
 
 ## Additional Features
 
-Beyond the flexible training scenarios available, the ML-Agents toolkit includes
+Beyond the flexible training scenarios available, the ML-Agents Toolkit includes
 additional features which improve the flexibility and interpretability of the
 training process.
 
@@ -370,7 +370,7 @@ training process.
 
 ## Summary and Next Steps
 
-To briefly summarize: The ML-Agents toolkit enables games and simulations built
+To briefly summarize: The ML-Agents Toolkit enables games and simulations built
 in Unity to serve as the platform for training intelligent agents. It is
 designed to enable a large variety of training modes and scenarios and comes
 packed with several features to enable researchers and developers to leverage

docs/Readme.md

@@ -66,7 +66,7 @@
 
 ## Translations
 
-To make the Unity ML-Agents toolkit accessible to the global research and Unity
+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 translating more pages and to

docs/Training-Imitation-Learning.md

@@ -27,7 +27,7 @@ See Behavioral Cloning + GAIL + Curiosity + RL below.
        width="700" border="0" />
 </p>
 
-The ML-Agents toolkit provides two features that enable your agent to learn from demonstrations.
+The ML-Agents Toolkit provides two features that enable your agent to learn from demonstrations.
 In most scenarios, you can combine these two features.
 
 * GAIL (Generative Adversarial Imitation Learning) uses an adversarial approach to

docs/Training-PPO.md

@@ -50,7 +50,7 @@ the agent for exploring new states, rather than just when an explicit reward is
 Furthermore, we could mix reward signals to help the learning process.
 
 Using `reward_signals` allows you to define [reward signals.](Reward-Signals.md)
-The ML-Agents toolkit provides three reward signals by default, the Extrinsic (environment)
+The ML-Agents Toolkit provides three reward signals by default, the Extrinsic (environment)
 reward signal, the Curiosity reward signal, which can be used to encourage exploration in
 sparse extrinsic reward environments, and the GAIL reward signal. Please see [Reward Signals](Reward-Signals.md)
 for additional details.

docs/Unity-Inference-Engine.md

@@ -1,12 +1,12 @@
 # Unity Inference Engine
 
-The ML-Agents toolkit allows you to use pre-trained neural network models
+The ML-Agents Toolkit allows you to use pre-trained neural network models
 inside your Unity games. This support is possible thanks to the Unity Inference
 Engine. The Unity Inference Engine is using
 [compute shaders](https://docs.unity3d.com/Manual/class-ComputeShader.html)
 to run the neural network within Unity.
 
-__Note__: The ML-Agents toolkit only supports the models created with our
+__Note__: The ML-Agents Toolkit only supports the models created with our
 trainers.
 
 ## Supported devices
@@ -40,6 +40,6 @@ tf2onnx does not currently support tensorflow 2.0.0 or later, or earlier than 1.
 When using a model, drag the model file into the **Model** field in the Inspector of the Agent.
 Select the **Inference Device** : CPU or GPU you want to use for Inference.
 
-**Note:** For most of the models generated with the ML-Agents toolkit, CPU will be faster than GPU.
+**Note:** For most of the models generated with the ML-Agents Toolkit, CPU will be faster than GPU.
 You should use the GPU only if you use the
 ResNet visual encoder or have a large number of agents with visual observations.

docs/Using-Tensorboard.md

@@ -1,6 +1,6 @@
 # Using TensorBoard to Observe Training
 
-The ML-Agents toolkit saves statistics during learning session that you can view
+The ML-Agents Toolkit saves statistics during learning session that you can view
 with a TensorFlow utility named,
 [TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard).
 
@@ -34,7 +34,7 @@ graphs.
 When you run the training program, `mlagents-learn`, you can use the
 `--save-freq` option to specify how frequently to save the statistics.
 
-## The ML-Agents toolkit training statistics
+## The ML-Agents Toolkit training statistics
 
 The ML-Agents training program saves the following statistics: