Supplementary Data for "Constrained collaborative optimization of charged particle tracking with multi-agent reinforcement learning"
Abstract
The artifacts included in this record complement the code repository available at https://github.com/SIVERT-pCT/marl-tracking and provide all necessary resources to reproduce the results and figures presented in [1]. These artifacts encompass trained models and supplementary files (e.g., loss landscape results) that support the execution of the experiments described in the repository. For comprehensive instructions on how to utilize this data, please consult the documentation provided at https://github.com/SIVERT-pCT/marl-tracking. [1] Kortus, T., Keidel, R., Gauger, N., Kieseler, J., on behalf of the Bergen pCT Collaboration (2026). Constrained collaborative optimization of charged particle tracking with multi-agent reinforcement learning. Machine Learning: Science and Technology, 7(1), 015021.
Fields of Study & Subjects
Particle Detector Development and Performance
Reinforcement Learning in Robotics
High-Energy Particle Collisions Research
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 2 February 2026