3 papers
cs.LG2026
RL-ABC: Reinforcement Learning for Accelerator Beamline Control
Anwar Ibrahim, Fedor Ratnikov, Maxim Kaledin +2
Particle accelerator beamline optimization is a high-dimensional control problem traditionally requiring significant expert intervention. We present RLABC (Reinforcement Learning f…
physics.acc-ph2025
Reinforcement Learning for Accelerator Beamline Control: a simulation-based approach
Anwar Ibrahim, Alexey Petrenko, Maxim Kaledin +3
Particle accelerators play a pivotal role in advancing scientific research, yet optimizing beamline configurations to maximize particle transmission remains a labor-intensive task…
physics.acc-ph2025
Optimisation of the Accelerator Control by Reinforcement Learning: A Simulation-Based Approach
Anwar Ibrahim, Denis Derkach, Alexey Petrenko +2
Optimizing accelerator control is a critical challenge in experimental particle physics, requiring significant manual effort and resource expenditure. Traditional tuning methods ar…