Home Registration Program Directions
Register now!
Call for abstracts extended! (new deadline 5 January 2024)

What is This Workshop about?

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.

Keynote speakers

We have a very exciting line-up of keynote speakers, and are very much looking forward to their talks!

Antonin Raffin
Research Engineer in Robotics and Machine Learning
German Aerospace Center
Maintainer of Stable Baselines3

Felix Berkenkamp
Lead Research Scientist
Bosch Center for AI
Safe exploration

Organizers

Sabrina Pochaba Sabrina Pochaba
University of Salzburg
Salzburg Research Simon Hirlaender Simon Hirlaender
University of Salzburg Andrea Santamaria Garcia Andrea Santamaria Garcia
Karlsruhe Institute of Technology (KIT) Chenran Xu Chenran Xu
Karlsruhe Institute of Technology (KIT) Jan Kaiser Jan Kaiser
Deutsches Elektronen-Synchrotron DESY Annika Eichler Annika Eichler
Deutsches Elektronen-Synchrotron DESY