Contribution

Contributing to CleanRL

👍🎉 Thank you for taking the time to contribute! 🎉👍

Feel free to open an issue or a Pull Request if you have any questions or suggestions. You can also join our Discord and ask questions there. If you plan to work on an issue, let us know in the issue thread to avoid duplicate work.

Good luck and have fun!

Dev Setup

poetry install
poetry install --with atari
poetry install --with pybullet

Then you can run the scripts under the poetry environment in two ways: poetry run or poetry shell.

poetry run: By prefixing poetry run, your command will run in poetry's virtual environment. For example, try running
```
poetry run python ppo.py
```
poetry shell: First, activate the poetry's virtual environment by executing poetry shell. Then, the name of the poetry's virtual environment (e.g. (cleanrl-ghSZGHE3-py3.9)) should appear in the left side of your shell. Afterwards, you can directly run
```
(cleanrl-ghSZGHE3-py3.9) python ppo.py
```

Pre-commit utilities

We use pre-commit to helps us automate a sequence of short tasks (called pre-commit "hooks") such as code formatting. In particular, we always use the following hooks when submitting code to the main repository.

pyupgrade: pyupgrade upgrades syntax for newer versions of the language.
isort: isort sorts imported dependencies according to their type (e.g, standard library vs third-party library) and name.
black: black enforces an uniform code style across the codebase.
autoflake: autoflake helps remove unused imports and variables.
codespell: codespell helps avoid common incorrect spelling.

You can run the following command to run the following hooks:

poetry run pre-commit run --all-files

which in most cases should automatically fix things as shown below:

Contributing new algorithms

We welcome the contributions of new algorithms.

Before opening a pull request, please open an issue first to discuss with us since this is likely a sizable effort. Once we agree on the plan, feel free to make a PR to include the new algorithm.

To help ease the review process, here is a checklist template when contributing a new algorithm. See https://github.com/vwxyzjn/cleanrl/pull/137 as an example.

[ ] I've read the CONTRIBUTION guide (required).
[ ] I have ensured pre-commit run --all-files passes (required).
[ ] I have contacted @vwxyzjn to obtain access to the openrlbenchmark W&B team (required).
[ ] I have tracked applicable experiments in openrlbenchmark/cleanrl with --capture-video flag toggled on (required).
[ ] I have updated the documentation and previewed the changes via mkdocs serve.
- [ ] I have explained note-worthy implementation details.
- [ ] I have explained the logged metrics.
- [ ] I have added links to the original paper and related papers (if applicable).
- [ ] I have added links to the PR related to the algorithm.
- [ ] I have created a table comparing my results against those from reputable sources (i.e., the original paper or other reference implementation).
- [ ] I have added the learning curves (in PNG format with width=500 and height=300).
- [ ] I have added links to the tracked experiments.
[ ] I have updated the tests accordingly (if applicable).