1st collaboration workshop on Reinforcement Learning for Autonomous Accelerators (RL4AA'23)
Building 345
Institute of Beam Physics and Technology (KIT)
Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.
Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.
RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.
We will take time for valuable discussions on the topic of RL applied to particle accelerators and to improve our common knowledge. We will also exchange ideas about promising future avenues and help consolidate a community to advance in this area of research in accelerator controls.
Adrian Oeftiger
Alexander Schütt
Andrea Santamaria Garcia
Annika Eichler
Anton Malygin
Awal Awal
Bianca Veglia
Chenran Xu
Christian Goffing
Conrad Caliari
Eleonora Vanzan
Felipe Donoso
Houssameddine Hoteit
Jan Henry Hetzel
Jan Kaiser
Johannes Steinmann
Julian Gethmann
Luca Scomparin
Lynda Boukela
Michael Schenk
Niky Bruchon
Nur Jomhari
Pierre Schnizer
Rene Förderer
Reuf Kozlica
Roberto Gómez-Espinosa Martín
Sabrina Appel
Sabrina Pochaba
Simon Hirlaender
Stephan-Robert Kötter
Waheedullah Sulaiman Khail