# 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.

## Communication

First, please read through our
[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
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

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).

When contributing to the project, please 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)
- Summary of the tests performed to validate your changes
- Issue numbers that the PR resolves (if any)

## 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 machine learning
algorithms. Feel free to submit these environments with a PR explaining the
nature of the environment and task.

## Continuous Integration (CI)

We run CircleCI on all PRs; all tests must be passing before the PR is merged.

Several static checks are run on the codebase using the
[pre-commit framework](https://pre-commit.com/) during CI. To execute the same
checks locally, install `pre-commit` and run `pre-commit run --all-files`. Some
hooks (for example, `black`) will output the corrected version of the code;
others (like `mypy`) may require more effort to fix.

### Code style

All python code should be formatted with
[`black`](https://github.com/ambv/black). Style and formatting for C# may be
enforced later.

### Python type annotations

We use [`mypy`](http://mypy-lang.org/) to perform static type checking on python
code. Currently not all code is annotated but we will increase coverage over
time. If you are adding or refactoring code, please

1. Add type annotations to the new or refactored code.
2. Make sure that code calling or called by the modified code also has type
   annotations.

The
[type hint cheat sheet](https://mypy.readthedocs.io/en/stable/cheat_sheet_py3.html)
provides a good introduction to adding type hints.

## Contributor License Agreements

When you open a pull request, you will be asked to acknolwedge our Contributor
License Agreement. We allow both individual contributions and contributions made
on behalf of companies. We use an open source tool called CLA assistant. If you
have any questions on our CLA, please
[submit an issue](https://github.com/Unity-Technologies/ml-agents/issues) or
email us at ml-agents@unity3d.com.