DataSheet1_Application of reinforcement learning in the LHC tune feedback.PDF
The Beam-Based Feedback System (BBFS) was primarily responsible for correcting the beam energy, orbit and tune in the CERN Large Hadron Collider (LHC). A major code renovation of the BBFS was planned and carried out during the LHC Long Shutdown 2 (LS2). This work consists of an explorative study to solve a beam-based control problem, the tune feedback (QFB), utilising state-of-the-art Reinforcement Learning (RL). A simulation environment was created to mimic the operation of the QFB. A series of RL agents were trained, and the best-performing agents were then subjected to a set of well-designed tests. The original feedback controller used in the QFB was reimplemented to compare the performance of the classical approach to the performance of selected RL agents in the test scenarios. Results from the simulated environment show that the RL agent performance can exceed the controller-based paradigm.
History
Usage metrics
Categories
- Biophysics
- Classical Physics not elsewhere classified
- Condensed Matter Physics not elsewhere classified
- Quantum Physics not elsewhere classified
- Solar System, Solar Physics, Planets and Exoplanets
- Mathematical Physics not elsewhere classified
- Classical and Physical Optics
- Astrophysics
- Photonics, Optoelectronics and Optical Communications
- Physical Chemistry of Materials
- Cloud Physics
- Tropospheric and Stratospheric Physics
- Physical Chemistry not elsewhere classified
- Applied Physics
- Computational Physics
- Condensed Matter Physics
- Particle Physics
- Plasma Physics
- High Energy Astrophysics; Cosmic Rays
- Mesospheric, Ionospheric and Magnetospheric Physics
- Space and Solar Physics