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Sample-efficient reinforcement learning for CERN accelerator control
Numerical optimization algorithms are already established tools to increase and stabilize the performance of particle accelerators. These algorithms have many advantages, are available out of the box, and can be adapted to a wide range of optimization problems in accelerator operation. The next boos...
| Autores principales: | , , , , , , |
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| Lenguaje: | eng |
| Publicado: |
2020
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1103/PhysRevAccelBeams.23.124801 http://cds.cern.ch/record/2747760 |