Collaboration Homepage: https://rl4aa.github.io/
- 1st RL4AA Workshop, Karlsruhe, 2023: Indico page
- 2nd RL4AA Workshop, Salzburg, 2024: Indico page
- 3rd RL4AA Worshop, Hamburg, 2025: Indico page
We welcome all enthusiasts and practitioners of reinforcement learning for particle accelerators to contribute to the website.
Find the contribution guide here.
The RL4AA collaboration provides advanced Python tutorials developed with care and dedication to foster learning and collaboration. The code and materials provided here are the result of significant effort, including state-of-the-art research and unpublished or pre-peer-reviewed work.
We share these resources in good faith, aiming to contribute to the community and advance knowledge in our field. If you use or build upon any part of this tutorial—whether in research, software, or educational materials—proper citation is required. Please cite the tutorial as indicated in the repository or its associated Zenodo entry.
While we encourage reuse and adaptation of our work, uncredited use or plagiarism is unacceptable. We actively monitor citations and expect users to engage in responsible scholarly practice. Failure to properly attribute this work may lead to formal actions.
By using this repository, you acknowledge and respect the effort behind it. We appreciate your support in maintaining academic integrity and fostering an open, collaborative environment.
Happy coding, and thank you for citing responsibly! 😊