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| Main Authors: | , , , |
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| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.18456346 |
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Table of Contents:
- <p>The artifacts included in this record complement the code repository available at <a href="https://github.com/SIVERT-pCT/marl-tracking" target="_new" rel="noopener">https://github.com/SIVERT-pCT/marl-tracking</a> 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 <a href="https://github.com/SIVERT-pCT/marl-tracking" target="_new" rel="noopener">https://github.com/SIVERT-pCT/marl-tracking</a>.</p> <p> </p> <p>[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<em>. Machine Learning: Science and Technology, 7(1), 015021.<br></em></p>