RL-ABC: Reinforcement learning for accelerator beamline control

Computer Physics Communications. 2026. Vol. 329. Article 110407.

Ibrahim A., Derkach D., Kaledin M., Ratnikov F., Petrenko A.

We present RLABC (Reinforcement Learning for Accelerator Beamline Control), an open-source Python framework that automatically transforms standard Elegant beamline configurations into reinforcement learning environments. RLABC integrates with the widely-used Elegant beam dynamics simulation code via SDDS-based interfaces, enabling researchers to apply modern RL algorithms to beamline optimization with minimal RL-specific development. Validation on a test beamline derived from the VEPP-5 injection complex (37 control parameters across 11 quadrupoles and 4 dipoles) demonstrates that the framework successfully enables RL-based optimization, with a Deep Deterministic Policy Gradient agent achieving 70% particle transmission. The complete framework, including configuration files and example notebooks, is available as open-source software to facilitate adoption and further research.

Keywords: Deep Reinforcement Learning, Particle accelerator physics, beamline optimization, машинное обучение с подкреплением, beam dynamics simulation, Gymnasium environment, ускоритель частиц, оптимизация пучкового канала, моделирование динамики пучка, среда Gymnasium