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CONTRIBUTING.md

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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 -E atari
poetry install -E 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 vdqn.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. (vectorized-value-methods-T4LxsEjE-py3.9)) should appear in the left side of your shell. Afterwards, you can directly run 
    python python vdqn.py

Code Formatting

We use Pre-commit to sort dependencies, remove unused variables and imports, format code using black, and check word spelling. You can run the following command:

poetry run pre-commit run --all-files

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:

  1. Code style: Make sure you match the code style used in other implemented algorithms in CleanRL. In particular, poetry run pre-commit run --all-files will help auto-format the code.
  2. Empirical analysis and benchmark: we adopt a similar guide from sb3-contrib with a bit of our spin. The implemented algorithm should come with tracked experiments that
    • match the reported performance in the paper (if applicable)
    • match the reported performance in a high-quality reference implementation (SB3, Tianshou, and others) (if applicable).
    • We should also add documentation on how exactly we want the tracked experiments to be done (i.e., what W&B project? should they capture video recording?)
  3. Documentation: the proposed algorithm should also come with documentation at https://docs.cleanrl.dev/rl-algorithms/ to
    • explain crucial implementation details
    • add links to the original paper and related papers (if applicable)
    • add links to the PR related to the algorithm
    • add links to the tracked experiments and benchmark results
  4. Tests: the proposed algorithm should come with an end-to-end test (see examples here) that ensures the algorithm does not crash. Other applicatable tests are also welcome.

Checklist:

Here is a checklist template when contributing a new algorithm. See #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).