master -> main. (#5010)

.github/PULL_REQUEST_TEMPLATE.md

 
 ### Checklist
 - [ ] Added tests that prove my fix is effective or that my feature works
-- [ ] Updated the [changelog](https://github.com/Unity-Technologies/ml-agents/blob/master/com.unity.ml-agents/CHANGELOG.md) (if applicable)
-- [ ] Updated the [documentation](https://github.com/Unity-Technologies/ml-agents/tree/master/docs) (if applicable)
-- [ ] Updated the [migration guide](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Migrating.md) (if applicable)
+- [ ] Updated the [changelog](https://github.com/Unity-Technologies/ml-agents/blob/main/com.unity.ml-agents/CHANGELOG.md) (if applicable)
+- [ ] Updated the [documentation](https://github.com/Unity-Technologies/ml-agents/tree/main/docs) (if applicable)
+- [ ] Updated the [migration guide](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Migrating.md) (if applicable)
 
 ### Other comments

.github/workflows/pre-commit.yml

 on:
   pull_request:
   push:
-    branches: [master]
+    branches: [main]
 
 jobs:
   pre-commit:

.github/workflows/publish_pypi.yaml

             package-path: [ml-agents, ml-agents-envs, gym-unity]
 
     steps:
-    - uses: actions/checkout@master
+    - uses: actions/checkout@main
     - name: Set up Python 3.7
       uses: actions/setup-python@v1
       with:

.github/workflows/pytest.yml

     - 'test_requirements.txt'
     - '.github/workflows/pytest.yml'
   push:
-    branches: [master]
+    branches: [main]
 
 jobs:
   pytest:

.yamato/com.unity.ml-agents-performance.yml

   triggers:
     cancel_old_ci: true
     recurring:
-      - branch: master
+      - branch: main
         frequency: daily
   artifacts:
     logs:

.yamato/com.unity.ml-agents-test.yml

   triggers:
     cancel_old_ci: true
     recurring:
-      - branch: master
+      - branch: main
         frequency: daily
 
 {% for package in packages %}
     cancel_old_ci: true
     {% if platform.name == "linux" %}
     expression: |
-      (pull_request.target eq "master" OR
+      (pull_request.target eq "main" OR
       pull_request.target match "release.+") AND
       NOT pull_request.draft AND
       (pull_request.changes.any match "com.unity.ml-agents/**" OR

.yamato/compressed-sensor-test.yml

     cancel_old_ci: true
     {% if editor.extra_test == "sensor" %}
     expression: |
-      (pull_request.target eq "master" OR
+      (pull_request.target eq "main" OR
       pull_request.target match "release.+") AND
       NOT pull_request.draft AND
       (pull_request.changes.any match "com.unity.ml-agents/**" OR

.yamato/gym-interface-test.yml

     cancel_old_ci: true
     {% if editor.extra_test == "gym" %}
     expression: |
-      (pull_request.target eq "master" OR
+      (pull_request.target eq "main" OR
       pull_request.target match "release.+") AND
       NOT pull_request.draft AND
       (pull_request.changes.any match "com.unity.ml-agents/**" OR

.yamato/protobuf-generation-test.yml

   triggers:
     cancel_old_ci: true
     expression: |
-      (pull_request.target eq "master" OR
+      (pull_request.target eq "main" OR
       pull_request.target match "release.+") AND
       NOT pull_request.draft AND
       (pull_request.changes.any match "protobuf-definitions/**" OR

.yamato/pytest-gpu.yml

   triggers:
     cancel_old_ci: true
     recurring:
-      - branch: master
+      - branch: main
         frequency: daily
   artifacts:
     logs:

.yamato/python-ll-api-test.yml

     cancel_old_ci: true
     {% if editor.extra_test == "llapi" %}
     expression: |
-      (pull_request.target eq "master" OR
+      (pull_request.target eq "main" OR
       pull_request.target match "release.+") AND
       NOT pull_request.draft AND
       (pull_request.changes.any match "com.unity.ml-agents/**" OR

.yamato/standalone-build-test.yml

   triggers:
     cancel_old_ci: true
     expression: |
-      (pull_request.target eq "master" OR
+      (pull_request.target eq "main" OR
       pull_request.target match "release.+") AND
       NOT pull_request.draft AND
       (pull_request.changes.any match "com.unity.ml-agents/**" OR

.yamato/standalone-build-webgl-test.yml

   triggers:
     cancel_old_ci: true
     recurring:
-      - branch: master
+      - branch: main
         frequency: weekly
   artifacts:
     logs:

.yamato/training-backcompat-tests.yml

   triggers:
     cancel_old_ci: true
     recurring:
-      - branch: master
+      - branch: main
         frequency: daily
   artifacts:
     logs:

.yamato/training-int-tests.yml

   triggers:
     cancel_old_ci: true
     expression: |
-      (pull_request.target eq "master" OR
+      (pull_request.target eq "main" OR
       pull_request.target match "release.+") AND
       NOT pull_request.draft AND
       (pull_request.changes.any match "com.unity.ml-agents/**" OR

README.md

 [here](https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/docs/Readme.md)
 to get started with the latest release of ML-Agents.**
 
-The table below lists all our releases, including our `master` branch which is
+The table below lists all our releases, including our `main` branch which is
 under active development and may be unstable. A few helpful guidelines:
 - The [Versioning page](docs/Versioning.md) overviews how we manage our GitHub
   releases and the versioning process for each of the ML-Agents components.
 
 | **Version** | **Release Date** | **Source** | **Documentation** | **Download** | **Python Package** | **Unity Package** |
 |:-------:|:------:|:-------------:|:-------:|:------------:|:------------:|:------------:|
-| **master (unstable)** | -- | [source](https://github.com/Unity-Technologies/ml-agents/tree/master) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/master/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/master.zip) | -- | -- |
+| **main (unstable)** | -- | [source](https://github.com/Unity-Technologies/ml-agents/tree/main) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/main/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/main.zip) | -- | -- |
 | **Release 13** | **February 17, 2021** | **[source](https://github.com/Unity-Technologies/ml-agents/tree/release_13)** | **[docs](https://github.com/Unity-Technologies/ml-agents/tree/release_13_docs/docs/Readme.md)** | **[download](https://github.com/Unity-Technologies/ml-agents/archive/release_13.zip)** | **[0.24.0](https://pypi.org/project/mlagents/0.24.0/)** | **[1.8.0](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.8/manual/index.html)** |
 | **Release 12** | December 22, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_12) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_12_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_12.zip) | [0.23.0](https://pypi.org/project/mlagents/0.23.0/) | [1.7.2](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.7/manual/index.html) |
 | **Release 11** | December 21, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_11) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_11_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_11.zip) | [0.23.0](https://pypi.org/project/mlagents/0.23.0/) | [1.7.0](https://docs.unity3d.com/Packages/com.unity.ml-agents@1.7/manual/index.html) |

com.unity.ml-agents/CHANGELOG.md

 - Added a `--torch-device` commandline option to `mlagents-learn`, which sets the default
   [`torch.device`](https://pytorch.org/docs/stable/tensor_attributes.html#torch.torch.device) used for training. (#4888)
 - The `--cpu` commandline option had no effect and was removed. Use `--torch-device=cpu` to force CPU training. (#4888)
-- The `mlagents_env` API has changed, `BehaviorSpec` now has a `observation_specs` property containing a list of `ObservationSpec`. For more information on `ObservationSpec` see [here](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Python-API.md#behaviorspec). (#4763, #4825)
+- The `mlagents_env` API has changed, `BehaviorSpec` now has a `observation_specs` property containing a list of `ObservationSpec`. For more information on `ObservationSpec` see [here](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Python-API.md#behaviorspec). (#4763, #4825)
 
 ### Bug Fixes
 #### com.unity.ml-agents (C#)
 #### com.unity.ml-agents (C#)
 #### ml-agents / ml-agents-envs / gym-unity (Python)
  - PyTorch trainers are now the default. See the
- [installation docs](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Installation.md) for
+ [installation docs](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Installation.md) for
  more information on installing PyTorch. For the time being, TensorFlow is still available;
  you can use the TensorFlow backend by adding `--tensorflow` to the CLI, or
  adding `framework: tensorflow` in the configuration YAML. (#4517)
 - The Barracuda dependency was upgraded to 1.1.2 (#4571)
 - Utilities were added to `com.unity.ml-agents.extensions` to make it easier to
-integrate with match-3 games. See the [readme](https://github.com/Unity-Technologies/ml-agents/blob/master/com.unity.ml-agents.extensions/Documentation~/Match3.md)
+integrate with match-3 games. See the [readme](https://github.com/Unity-Technologies/ml-agents/blob/main/com.unity.ml-agents.extensions/Documentation~/Match3.md)
 for more details. (#4515)
 #### ml-agents / ml-agents-envs / gym-unity (Python)
 - The `action_probs` node is no longer listed as an output in TensorFlow models (#4613).
 #### ml-agents / ml-agents-envs / gym-unity (Python)
  - Added the Random Network Distillation (RND) intrinsic reward signal to the Pytorch
  trainers. To use RND, add a `rnd` section to the `reward_signals` section of your
- yaml configuration file. [More information here](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Training-Configuration-File.md#rnd-intrinsic-reward) (#4473)
+ yaml configuration file. [More information here](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Training-Configuration-File.md#rnd-intrinsic-reward) (#4473)
 ### Minor Changes
 #### com.unity.ml-agents (C#)
  - Stacking for compressed observations is now supported. An additional setting
 ### Major Changes
 #### ml-agents / ml-agents-envs / gym-unity (Python)
 - The Parameter Randomization feature has been refactored to enable sampling of new parameters per episode to improve robustness. The
-  `resampling-interval` parameter has been removed and the config structure updated. More information [here](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Training-ML-Agents.md). (#4065)
+  `resampling-interval` parameter has been removed and the config structure updated. More information [here](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Training-ML-Agents.md). (#4065)
-[here](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Training-ML-Agents.md).(#4160)
+[here](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Training-ML-Agents.md).(#4160)
 
 ### Minor Changes
 #### com.unity.ml-agents (C#)

com.unity.ml-agents/CONTRIBUTING.md

 ## Communication
 
 First, please read through our
-[code of conduct](https://github.com/Unity-Technologies/ml-agents/blob/master/CODE_OF_CONDUCT.md),
+[code of conduct](https://github.com/Unity-Technologies/ml-agents/blob/main/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
 
 ## Git Branches
 
-The master branch corresponds to the most recent version of the project. Note
+The main branch corresponds to the most recent version of the project. Note
 that this may be newer that the
 [latest release](https://github.com/Unity-Technologies/ml-agents/releases/tag/latest_release).
 

docs/Installation-Anaconda-Windows.md

 ```
 
 The `--branch release_13` option will switch to the tag of the latest stable
-release. Omitting that will get the `master` branch which is potentially
+release. Omitting that will get the `main` branch which is potentially
 unstable.
 
 If you don't want to use Git, you can find download links on the

docs/Installation.md

 ```
 
 The `--branch release_13` option will switch to the tag of the latest stable
-release. Omitting that will get the `master` branch which is potentially
+release. Omitting that will get the `main` branch which is potentially
 unstable.
 
 #### Advanced: Local Installation for Development
-back, make sure to clone the `master` branch (by omitting `--branch release_13`
+back, make sure to clone the `main` branch (by omitting `--branch release_13`
 from the command above). See our
 [Contributions Guidelines](../com.unity.ml-agents/CONTRIBUTING.md) for more
 information on contributing to the ML-Agents Toolkit.

docs/Migrating.md

 - The Parameter Randomization feature has been merged with the Curriculum feature. It is now possible to specify a sampler
 in the lesson of a Curriculum. Curriculum has been refactored and is now specified at the level of the parameter, not the
 behavior. More information
-[here](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Training-ML-Agents.md).(#4160)
+[here](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Training-ML-Agents.md).(#4160)
 
 ### Steps to Migrate
 - The configuration format for curriculum and parameter randomization has changed. To upgrade your configuration files,

docs/localized/KR/docs/Installation-Anaconda-Windows.md

 git clone https://github.com/Unity-Technologies/ml-agents.git
 ```
 
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+���� Git�� �����ϰ� ���� �ʴٸ� ������ [��ũ](https://github.com/Unity-Technologies/ml-agents/archive/main.zip)���� ���� ������ �ٿ��ε� �� �� �ֽ��ϴ�.
 
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docs/localized/RU/README.md

-<img src="https://github.com/Unity-Technologies/ml-agents/blob/master/docs/images/image-banner.png" align="middle" width="3000"/>
+<img src="https://github.com/Unity-Technologies/ml-agents/blob/main/docs/images/image-banner.png" align="middle" width="3000"/>
 
 # Unity ML-Agents Toolkit Version Release 7
 
 См. [здесь](https://github.com/Unity-Technologies/ml-agents/tree/release_7_docs/docs/Readme.md),
 чтобы начать работать с самой последней версий ML-Agents.**
 
-Таблица внизу - список всех наших релизов, включая master ветку, над которой мы ведем активную работу
+Таблица внизу - список всех наших релизов, включая main ветку, над которой мы ведем активную работу
 и которая может быть нестабильной. Полезная информация:
 
 [Управление версиями](docs/Versioning.md) - описание того, как мы работам с GitHub.
 
 | **Version** | **Дата релиза** | **Source** | **Документация** | **Загрузка** |
 |:-------:|:------:|:-------------:|:-------:|:------------:|
-| **master (unstable)** | -- | [source](https://github.com/Unity-Technologies/ml-agents/tree/master) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/master/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/master.zip) |
+| **main (unstable)** | -- | [source](https://github.com/Unity-Technologies/ml-agents/tree/main) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/main/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/main.zip) |
 | **Release 7** | **16 Сентября, 2020** | **[source](https://github.com/Unity-Technologies/ml-agents/tree/release_7)** | **[docs](https://github.com/Unity-Technologies/ml-agents/tree/release_7_docs/docs/Readme.md)** | **[download](https://github.com/Unity-Technologies/ml-agents/archive/release_7.zip)** |
 | **Release 6** | 12 Августа, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_6) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_6_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_6.zip) |
 | **Release 5** | 31 Июля, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_5) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_5_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_5.zip) |

docs/localized/RU/docs/Начало работы.md

 # Начало работы
 
-В данной статье мы разберем шаг за шагом один из [наших примеров](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Learning-Environment-Examples.md),
+В данной статье мы разберем шаг за шагом один из [наших примеров](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Learning-Environment-Examples.md),
-[Background: Unity](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Background-Unity.md).
+[Background: Unity](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Background-Unity.md).
-[Background: Machine Learning](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Background-Machine-Learning.md).
+[Background: Machine Learning](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Background-Machine-Learning.md).
-![Головы с мячами](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/images/balance.png)
+![Головы с мячами](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/images/balance.png)
 
 Нашей сценой будет **3D Balance Ball**. Агенты в ней - синие кубы-платформы, у каждого
 на голове по мячу. Все они - копии друг друга. Каждый куб-агент пытается
 
 ## Установка
 Если вы еще не установили ML-Agents, следуйте этой
-инструкции по установке(https://github.com/Unity-Technologies/ml-agents/tree/master/docs/localized/RU/docs/Установка.md).
+инструкции по установке(https://github.com/Unity-Technologies/ml-agents/tree/main/docs/localized/RU/docs/Установка.md).
 
 1. Далее, откройте в Unity Project, в котором находятся примеры:
 1. Запустите Unity Hub
 со _средой_, собирая о ней данные. В Unity, среда это сцена (scene), в которой есть один
 или более объектов - Агентов, а также, конечно, и другие объекты, с которыми взаимодействует агент.
 
-![Unity-Editor](https://raw.githubusercontent.com/Unity-Technologies/ml-agents/master/docs/images/mlagents-3DBallHierarchy.png)
+![Unity-Editor](https://raw.githubusercontent.com/Unity-Technologies/ml-agents/main/docs/images/mlagents-3DBallHierarchy.png)
 
 Обратите внимание: В Unity каждый объект сцены - это объект класса GameObject. GameObject это
 буквально контейнер для всего, что касается объекта: его физики, графики, поведения и пр.,
 ## Запуск заранее обученной (предтренированной) модели
 
 Мы включили в свои примеры заранее обученные модели (файлы с расширением `.nn`)
-и использовали [Unity Inference Engine](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Unity-Inference-Engine.md),
+и использовали [Unity Inference Engine](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Unity-Inference-Engine.md),
 чтобы запустить их в Unity. В этом разделе мы воспользуемся одной
 из таких моделей для 3D Ball.
 
    Поэтому при необходимости внести изменения в каждую из платформ,
    вы можете просто изменить сам префаб вместо этого.
 
-   ![Platform Prefab](https://raw.githubusercontent.com/Unity-Technologies/ml-agents/master/docs/images/platform_prefab.png)
+   ![Platform Prefab](https://raw.githubusercontent.com/Unity-Technologies/ml-agents/main/docs/images/platform_prefab.png)
-   ![3dball learning brain](https://raw.githubusercontent.com/Unity-Technologies/ml-agents/master/docs/images/3dball_learning_brain.png)
+   ![3dball learning brain](https://raw.githubusercontent.com/Unity-Technologies/ml-agents/main/docs/images/3dball_learning_brain.png)
 
 1. Теперь каждый `Агент` на каждой платформе `3DBall` в окне **Hierarchy**
    должен содержать модель поведения **3DBall** в `Behavior Parameters`.
 фазы обучения к другой. Это сигнал, что процесс обучения проходит успешно.
 
 **Примечание:** Вы можете обучать агента, используя вместо Editor’a исполняемые файлы.
-См. инструкцию [Using an Executable](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Learning-Environment-Executable.md).
+См. инструкцию [Using an Executable](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Learning-Environment-Executable.md).
 
 ### Наблюдение за тренировочным процессом
 
 (среда/суммарная награда за эпизод), которая должно увеличиваться в процессе обучения,
 приближаясь к 100 - максимально возможное значение, которого может достигнуть агент.
 
-![Example TensorBoard Run](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/images/mlagents-TensorBoard.png)
+![Example TensorBoard Run](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/images/mlagents-TensorBoard.png)
 
 ## Внедрение модели в среду Unity
 
 ### Следующие шаги
 
 - Для дополнительной информации о ML-Agents Toolkit,
-  см. [Обзор ML-Agents Toolkit](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/ML-Agents-Overview.md).
-- [Создание своих сцен](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Learning-Environment-Create-New.md)
+  см. [Обзор ML-Agents Toolkit](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/ML-Agents-Overview.md).
+- [Создание своих сцен](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Learning-Environment-Create-New.md)
-  примера в [ML-Agents - Example Environments](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Learning-Environment-Examples.md)
-- Информация про различные опции обучения - [Training ML-Agents](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Training-ML-Agents.md)
+  примера в [ML-Agents - Example Environments](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Learning-Environment-Examples.md)
+- Информация про различные опции обучения - [Training ML-Agents](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Training-ML-Agents.md)

docs/localized/RU/docs/Установка.md

 
 ML-Agents Toolkit состоит из нескольких компонентов:
 
-- Специальный пакет Unity ([`com.unity.ml-agents`](https://github.com/Unity-Technologies/ml-agents/tree/master/com.unity.ml-agents))
+- Специальный пакет Unity ([`com.unity.ml-agents`](https://github.com/Unity-Technologies/ml-agents/tree/main/com.unity.ml-agents))
-  - ([`mlagents`](https://github.com/Unity-Technologies/ml-agents/tree/master/ml-agents)) - пакет с алгоритмами машинного обучения,
+  - ([`mlagents`](https://github.com/Unity-Technologies/ml-agents/tree/main/ml-agents)) - пакет с алгоритмами машинного обучения,
-  - ([`mlagents_envs`](https://github.com/Unity-Technologies/ml-agents/tree/master/ml-agents-envs) содержит Python
+  - ([`mlagents_envs`](https://github.com/Unity-Technologies/ml-agents/tree/main/ml-agents-envs) содержит Python
-  - ([`gym_unity`](https://github.com/Unity-Technologies/ml-agents/tree/master/gym-unity)) - позволяет обернуть вашу сцену
+  - ([`gym_unity`](https://github.com/Unity-Technologies/ml-agents/tree/main/gym-unity)) - позволяет обернуть вашу сцену
-- Unity [Project](https://github.com/Unity-Technologies/ml-agents/tree/master/Project),
-содержащий [примеры сцены](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Learning-Environment-Examples.md),
+- Unity [Project](https://github.com/Unity-Technologies/ml-agents/tree/main/Project),
+содержащий [примеры сцены](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Learning-Environment-Examples.md),
 где реализованы различные возможности ML-Agents для наглядности.
 
 Итого, чтобы установить и использовать ML-Agents, вам нужно:
 
 Вам нужно будет склонировать репозиторий, если вы планируете вносить изменения
 или что-то добавлять в ML-Agents для своих целей. Или вы планируете сделать
-эти изменения общедоступными, пожалуйста, склонируйте master branch
+эти изменения общедоступными, пожалуйста, склонируйте main branch
-Далее см. [гайд про публикацию правок](https://github.com/Unity-Technologies/ml-agents/blob/master/com.unity.ml-agents/CONTRIBUTING.md).
+Далее см. [гайд про публикацию правок](https://github.com/Unity-Technologies/ml-agents/blob/main/com.unity.ml-agents/CONTRIBUTING.md).
 
 ### Установка пакета `com.unity.ml-agents` для Unity
 Unity ML-Agents C# SDK это пакет Unity. Вы можете установить его прямо из пакетного менеджера,
 А в Unity 2019.3 она - сверху слева.
 
 <p align="center">
-  <img src="https://raw.githubusercontent.com/Unity-Technologies/ml-agents/master/docs/images/unity_package_manager_window.png"
+  <img src="https://raw.githubusercontent.com/Unity-Technologies/ml-agents/main/docs/images/unity_package_manager_window.png"
-  <img src="https://raw.githubusercontent.com/Unity-Technologies/ml-agents/master/docs/images/unity_package_json.png"
+  <img src="https://raw.githubusercontent.com/Unity-Technologies/ml-agents/main/docs/images/unity_package_json.png"
      alt="package.json"
      height="300"
      border="10" />
 Virtual Environments позволяет управлять ранее установленными версиями так,
 что для одного проекта будут одни зависимости, а для другого - другие на каждой
 из операционных систем - Mac / Windows / Linux.
-Гайд по [Virtual Environments](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Using-Virtual-Environment.md).
+Гайд по [Virtual Environments](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Using-Virtual-Environment.md).
 
 Чтобы установить `mlagents` Python, активируйте вашу виртуальную среду
 и выполните следующее в командной строке:
 команду `mlagents-learn --help`, после
 исполнения которой вы увидите набор доступных команд `mlagents-learn`.
 Устанавливая mlagents, вы также устанавливаете и все то, на базе чего он сделан -
-см. [setup.py file](https://github.com/Unity-Technologies/ml-agents/blob/master/ml-agents/setup.py).
-В том числе [TensorFlow](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Background-TensorFlow.md)
+см. [setup.py file](https://github.com/Unity-Technologies/ml-agents/blob/main/ml-agents/setup.py).
+В том числе [TensorFlow](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Background-TensorFlow.md)
 (требует поддержки CPU w/ AVX).
 
 #### Продвинутая локальная установка для разработки
 
 ### Следующие шаги
 
-Гайд ["Начало работы"](https://github.com/Unity-Technologies/ml-agents/tree/master/docs/localized/RU/docs/Начало-работы.md)
+Гайд ["Начало работы"](https://github.com/Unity-Technologies/ml-agents/tree/main/docs/localized/RU/docs/Начало-работы.md)
 содержит в себе серию коротких обучающих уроков по настройке ML-Agents внутри Unity
 и запуск предобученной модели, а также уроки по созданию и дополнению сцен,
 где будет обучаться ваш агент.
-Если у вас возникли трудности с ML-Agents, пожалуйста, обратитесь к [FAQ](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/FAQ.md)
-и к странице [Limitations](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Limitations.md).
+Если у вас возникли трудности с ML-Agents, пожалуйста, обратитесь к [FAQ](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/FAQ.md)
+и к странице [Limitations](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Limitations.md).
 Если это не решило вашу проблему, создайте запрос [здесь](https://github.com/Unity-Technologies/ml-agents/issues),
 предоставив информацию о операционной системе, версии Python и сообщение об ошибки (если оно есть).
 

ml-agents/setup.py

         "protobuf>=3.6",
         "pyyaml>=3.1.0",
         # Windows ver. of PyTorch doesn't work from PyPi. Installation:
-        # https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Installation.md#windows-installing-pytorch
+        # https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Installation.md#windows-installing-pytorch
         'torch>=1.6.0,<1.8.0;platform_system!="Windows"',
         "tensorboard>=1.15",
         # cattrs 1.1.0 dropped support for python 3.6.

utils/make_readme_table.py

         f"{bold_str}[docs]({version_info.doc_link}){bold_str}",
         f"{bold_str}[download]({version_info.download_link}){bold_str}",
     ]
-    if version_info.is_master:
+    if version_info.is_main:
         cells.append("--")  # python
         cells.append("--")  # Unity
     else:
         return LooseVersion(self.python_verion)
 
     @property
-    def is_master(self) -> bool:
-        return self.release_tag == "master"
+    def is_main(self) -> bool:
+        return self.release_tag == "main"
-        if self.is_master:
+        if self.is_main:
             return datetime.today()
         return datetime.strptime(self.release_date, "%B %d, %Y")
 
         """
         if self.is_verified:
             return f"Verified Package {self.csharp_version}"
-        elif self.is_master:
-            return "master (unstable)"
+        elif self.is_main:
+            return "main (unstable)"
         else:
             return self.release_tag.replace("_", " ").title()
 
 
 
 versions = [
-    ReleaseInfo("master", "master", "master", "--"),
+    ReleaseInfo("main", "main", "main", "--"),
     ReleaseInfo("release_1", "1.0.0", "0.16.0", "April 30, 2020"),
     ReleaseInfo("release_2", "1.0.2", "0.16.1", "May 20, 2020"),
     ReleaseInfo("release_3", "1.1.0", "0.17.0", "June 10, 2020"),
 highlight_versions.add([v for v in sorted_versions if v.is_verified][0])
 # Highlight the most recent regular version
 highlight_versions.add(
-    [v for v in sorted_versions if (not v.is_verified and not v.is_master)][0]
+    [v for v in sorted_versions if (not v.is_verified and not v.is_main)][0]
 )
 
 count_by_verified = Counter()

utils/validate_release_links.py

 def get_release_tag() -> Optional[str]:
     """
     Returns the release tag for the mlagents python package.
-    This will be None on the master branch.
+    This will be None on the main branch.
     :return:
     """
     with open(TRAINER_INIT_FILE) as f: