TY  - CONF
AU  - Santamaria Garcia, Andrea
AU  - Scomparin, Luca
AU  - Xu, Chenran
AU  - Hirlaender, Simon
AU  - Pochaba, Sabrina
AU  - Eichler, Annika
AU  - Kaiser, Jan
AU  - Schenk, Michael
TI  - The Reinforcement Learning for Autonomous Accelerators Collaboration
CY  - Geneva, Switzerland
PB  - JACoW Publishing
M1  - PUBDB-2024-01721
SN  - 978-3-95450-247-9
SP  - 1812 - 1815 
PY  - 2024
N1  - Literaturangaben;
AB  - Reinforcement Learning (RL) is a unique learning paradigm that is particularly well-suited to tackle complex control tasks, can deal with delayed consequences, and can learn from experience without an explicit model of the dynamics of the problem. These properties make RL methods extremely promising for applications in particle accelerators, where the dynamically evolving conditions of both the particle beam and the accelerator systems must be constantly considered. While the time to work on RL is now particularly favorable thanks to the availability of high-level programming libraries and resources, its implementation in particle accelerators is not trivial and requires further consideration. In this context, the Reinforcement Learning for Autonomous Accelerators (RL4AA) international collaboration was established to consolidate existing knowledge, share experiences and ideas, and collaborate on accelerator-specific solutions that leverage recent advances in RL. Here we report on two collaboration workshops, RL4AA'23 and RL4AA'24, which took place in February 2023 at the Karlsruhe Institute of Technology and in February 2024 at the Paris-Lodron Universität Salzburg.
T2  - 15th International Particle Accelerator Conference
CY  - 19 May 2024 - 24 May 2024, Nashville (USA)
Y2  - 19 May 2024 - 24 May 2024
M2  - Nashville, USA
KW  - Accelerator Physics (Other)
KW  - mc6-beam-instrumentation-controls-feedback-and-operational-aspects - MC6: Beam Instrumentation, Controls, Feedback, and Operational Aspects (Other)
KW  - MC6.D13 - MC6.D13 Machine Learning (Other)
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
DO  - DOI:10.18429/JACOW-IPAC2024-TUPS62
UR  - https://bib-pubdb1.desy.de/record/607016
ER  -