1–6 Jun 2025
Taipei International Convention Center (TICC)
Asia/Taipei timezone

Reinforcement learning in particle accelerators

THYD1
5 Jun 2025, 11:00
30m
Room 101, First Floor (TICC)

Room 101, First Floor

TICC

Speaker

Dr Andrea Santamaria Garcia (University of Liverpool)

Description

Reinforcement learning (RL) is a unique learning paradigm inspired by the behaviour of animals and humans to learn to solve tasks autonomously. Learning occurs through interactions with an environment, exploring, and evaluating strategies under various conditions. RL excels in complex environments, can handle delayed consequences, and is able to learn solely from experience without access to an explicit model of the system. This makes RL particularly promising for particle accelerators, where the dynamic conditions of particle beams and accelerator systems require continuous adaptation, and modelling is challenging. Although RL applications are emerging in accelerator physics and showing promising results, their widespread introduction faces critical challenges. Among the main obstacles are the effective formulation of control problems, training, and the deployment of solutions in real systems. This paper provides an overview of the potential of RL in accelerator applications, highlighting current challenges and future research directions.

Region represented Europe
Paper preparation format LaTeX

Author

Dr Andrea Santamaria Garcia (University of Liverpool)

Co-authors

Annika Eichler (Deutsches Elektronen-Synchrotron DESY) Chenran Xu (Karlsruhe Institute of Technology) Jan Kaiser (Deutsches Elektronen-Synchrotron DESY) Simon Hirlaender (University of Salzburg)

Presentation materials

There are no materials yet.