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Release 13 versions. (#4946)
- updated release tag validation script to automate the updating of files with release tags that need to be changed as part of the pre-commit operation.
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‎README.md

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# Unity ML-Agents Toolkit
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[![docs badge](https://img.shields.io/badge/docs-reference-blue.svg)](https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/docs/)
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[![docs badge](https://img.shields.io/badge/docs-reference-blue.svg)](https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/docs/)
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[![license badge](https://img.shields.io/badge/license-Apache--2.0-green.svg)](LICENSE)
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## Releases & Documentation
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**Our latest, stable release is `Release 12`. Click
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[here](https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/docs/Readme.md)
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[here](https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/docs/Readme.md)
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to get started with the latest release of ML-Agents.**
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The table below lists all our releases, including our `master` branch which is

‎com.unity.ml-agents.extensions/Documentation~/Grid-Sensor.md

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An image can be thought of as a matrix of a predefined width (W) and a height (H) and each pixel can be thought of as simply an array of length 3 (in the case of RGB), `[Red, Green, Blue]` holding the different channel information of the color (channel) intensities at that pixel location. Thus an image is just a 3 dimensional matrix of size WxHx3. A Grid Observation can be thought of as a generalization of this setup where in place of a pixel there is a "cell" which is an array of length N representing different channel intensities at that cell position. From a Convolutional Neural Network point of view, the introduction of multiple channels in an "image" isn't a new concept. One such example is using an RGB-Depth image which is used in several robotics applications. The distinction of Grid Observations is what the data within the channels represents. Instead of limiting the channels to color intensities, the channels within a cell of a Grid Observation generalize to any data that can be represented by a single number (float or int).
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Before jumping into the details of the Grid Sensor, an important thing to note is the agent performance and qualitatively different behavior over raycasts. Unity MLAgent's comes with a suite of example environments. One in particular, the [Food Collector](https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/docs/Learning-Environment-Examples.md#food-collector), has been the focus of the Grid Sensor development.
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Before jumping into the details of the Grid Sensor, an important thing to note is the agent performance and qualitatively different behavior over raycasts. Unity MLAgent's comes with a suite of example environments. One in particular, the [Food Collector](https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/docs/Learning-Environment-Examples.md#food-collector), has been the focus of the Grid Sensor development.
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The Food Collector environment can be described as:
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* Set-up: A multi-agent environment where agents compete to collect food.

‎com.unity.ml-agents.extensions/Documentation~/Match3.md

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This implementation includes:
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* C# implementation catered toward a Match-3 setup including concepts around encoding for moves based on [Human Like Playtesting with Deep Learning](https://www.researchgate.net/publication/328307928_Human-Like_Playtesting_with_Deep_Learning)
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* An example Match-3 scene with ML-Agents implemented (located under /Project/Assets/ML-Agents/Examples/Match3). More information, on Match-3 example [here](https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/docs/docs/Learning-Environment-Examples.md#match-3).
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* An example Match-3 scene with ML-Agents implemented (located under /Project/Assets/ML-Agents/Examples/Match3). More information, on Match-3 example [here](https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/docs/docs/Learning-Environment-Examples.md#match-3).
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### Feedback
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If you are a Match-3 developer and are trying to leverage ML-Agents for this scenario, [we want to hear from you](https://forms.gle/TBsB9jc8WshgzViU9). Additionally, we are also looking for interested Match-3 teams to speak with us for 45 minutes. If you are interested, please indicate that in the [form](https://forms.gle/TBsB9jc8WshgzViU9). If selected, we will provide gift cards as a token of appreciation.

‎com.unity.ml-agents.extensions/Documentation~/com.unity.ml-agents.extensions.md

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recommended ways to install the package:
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### Local Installation
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[Clone the repository](https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/docs/Installation.md#clone-the-ml-agents-toolkit-repository-optional) and follow the
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[Local Installation for Development](https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/docs/Installation.md#advanced-local-installation-for-development-1)
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[Clone the repository](https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/docs/Installation.md#clone-the-ml-agents-toolkit-repository-optional) and follow the
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[Local Installation for Development](https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/docs/Installation.md#advanced-local-installation-for-development-1)
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directions (substituting `com.unity.ml-agents.extensions` for the package name).
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### Github via Package Manager
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In Unity 2019.4 or later, open the Package Manager, hit the "+" button, and select "Add package from git URL".
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![Package Manager git URL](https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/docs/images/unity_package_manager_git_url.png)
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![Package Manager git URL](https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/docs/images/unity_package_manager_git_url.png)
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In the dialog that appears, enter
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```
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## Need Help?
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The main [README](https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/README.md) contains links for contacting the team or getting support.
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The main [README](https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/README.md) contains links for contacting the team or getting support.

‎com.unity.ml-agents.extensions/package.json

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"unity": "2018.4",
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"description": "A source-only package for new features based on ML-Agents",
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"dependencies": {
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"com.unity.ml-agents": "1.7.2-preview"
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"com.unity.ml-agents": "1.8.0-preview"
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}
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}

‎com.unity.ml-agents/CHANGELOG.md

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and this project adheres to
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[Semantic Versioning](http://semver.org/spec/v2.0.0.html).
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## [Unreleased]
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### Major Changes
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#### com.unity.ml-agents (C#)
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#### ml-agents / ml-agents-envs / gym-unity (Python)
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### Minor Changes
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#### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
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#### ml-agents / ml-agents-envs / gym-unity (Python)
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### Bug Fixes
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#### com.unity.ml-agents (C#)
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#### ml-agents / ml-agents-envs / gym-unity (Python)
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## [1.8.0-preview] - 2021-02-17
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### Major Changes
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#### com.unity.ml-agents (C#)

‎com.unity.ml-agents/Documentation~/com.unity.ml-agents.md

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[unity ML-Agents Toolkit]: https://github.com/Unity-Technologies/ml-agents
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[unity inference engine]: https://docs.unity3d.com/Packages/com.unity.barracuda@latest/index.html
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[package manager documentation]: https://docs.unity3d.com/Manual/upm-ui-install.html
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[installation instructions]: https://github.com/Unity-Technologies/ml-agents/blob/release_12_docs/docs/Installation.md
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[installation instructions]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Installation.md
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[github repository]: https://github.com/Unity-Technologies/ml-agents
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[python package]: https://github.com/Unity-Technologies/ml-agents
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[execution order of event functions]: https://docs.unity3d.com/Manual/ExecutionOrder.html

‎com.unity.ml-agents/Runtime/Academy.cs

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* API. For more information on each of these entities, in addition to how to
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* set-up a learning environment and train the behavior of characters in a
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* Unity scene, please browse our documentation pages on GitHub:
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* https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/docs/
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* https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/docs/
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*/
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/// fall back to inference or heuristic decisions. (You can also set agents to always use
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/// inference or heuristics.)
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/// </remarks>
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[HelpURL("https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/" +
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[HelpURL("https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/" +
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internal const string k_PackageVersion = "1.7.2-preview";
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internal const string k_PackageVersion = "1.8.0-preview";
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‎com.unity.ml-agents/Runtime/Actuators/IActionReceiver.cs

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/// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_12_docs/docs/Learning-Environment-Design-Agents.md#actions
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/// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Learning-Environment-Design-Agents.md#actions
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‎com.unity.ml-agents/Runtime/Actuators/IDiscreteActionMask.cs

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/// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Learning-Environment-Design-Agents.md#actions
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‎com.unity.ml-agents/Runtime/Agent.cs

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/// [OnDisable()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnDisable.html]
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/// [OnBeforeSerialize()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnBeforeSerialize.html
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/// [OnAfterSerialize()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnAfterSerialize.html
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/// [Agents]: https://github.com/Unity-Technologies/ml-agents/blob/release_12_docs/docs/Learning-Environment-Design-Agents.md
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/// [Reinforcement Learning in Unity]: https://github.com/Unity-Technologies/ml-agents/blob/release_12_docs/docs/Learning-Environment-Design.md
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/// [Agents]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Learning-Environment-Design-Agents.md
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/// [Reinforcement Learning in Unity]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Learning-Environment-Design.md
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/// [Unity ML-Agents Toolkit]: https://github.com/Unity-Technologies/ml-agents
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/// [Unity ML-Agents Toolkit manual]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Readme.md
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/// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Learning-Environment-Design-Agents.md#rewards
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/// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/ML-Agents-Overview.md#a-quick-note-on-reward-signals
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/// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Learning-Environment-Design-Agents.md#rewards
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/// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/ML-Agents-Overview.md#a-quick-note-on-reward-signals
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/// [Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Learning-Environment-Design-Agents.md#actions
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/// [Observations and Sensors]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Learning-Environment-Design-Agents.md#observations-and-sensors
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/// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs/Learning-Environment-Design-Agents.md#actions
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‎com.unity.ml-agents/Runtime/Demonstrations/DemonstrationRecorder.cs

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/// [Imitation Learning - Recording Demonstrations]: https://github.com/Unity-Technologies/ml-agents/blob/release_13_docs/docs//Learning-Environment-Design-Agents.md#recording-demonstrations
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‎com.unity.ml-agents/Runtime/DiscreteActionMasker.cs

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‎com.unity.ml-agents/package.json

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‎docs/Installation-Anaconda-Windows.md

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