RL-ABC is a framework that formulates accelerator beamline tuning as a Markov decision process with a 57-dimensional state and configurable reward, enabling a DDPG agent to reach 70.3% particle transmission on a VEPP-5 test beamline, matching differential evolution.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
RL-ABC: Reinforcement Learning for Accelerator Beamline Control
RL-ABC is a framework that formulates accelerator beamline tuning as a Markov decision process with a 57-dimensional state and configurable reward, enabling a DDPG agent to reach 70.3% particle transmission on a VEPP-5 test beamline, matching differential evolution.