We are very happy that you have decided to help us democratize meta-reinforcement learning research and experimentation. We highly welcome:
- Tests 🥺
- Bug reports and pull requests with fixes
- Performance and speed optimizations
- Documentation and comments
- Additional rules and goals, game mechanics (without significant overhead)
Do not be afraid to ask questions and share new ideas about possible directions for XLand-MiniGrid!
Contributing code is done through standard github methods:
- Fork this repo
- Make a change and commit your code
- Submit a pull request. It will be reviewed by maintainers, and they'll give feedback or make requests as applicable
git clone [email protected]:corl-team/xland-minigrid.git
cd xland-minigrid
pip install -e ".[dev]"
We use awesome Ruff linter and formatter and Pyright for type checking. The CI will run several checks on the new code pushed to the repository. These checks can also be run locally without waiting for the CI by following the steps below:
- install pre-commit
- install the Git hooks by running
pre-commit install
Once those two steps are done, the Git hooks will be run automatically at
every new commit. The Git hooks can also be run manually with
pre-commit run --all-files
, and if needed they can be
skipped (not recommended) with git commit --no-verify
.
Be sure to run and fix all issues from the pre-commit run --all-files
before the push!
If you want to see possible problems before pre-commit, you can run ruff check --diff .
and ruff format --check
to see exact linter and formatter suggestions and possible fixes.
Similarly, run pyright src/xminigrid
to see possible problems with type hints.
All contributions will fall under the project's original license.