# Reinforcement Learning for Accelerator Beamline Control: a simulation-based approach

arXiv: https://arxiv.org/abs/2510.26805 · HTML: https://mlsanity.com/paper/2510.26805/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 18 Oct 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 3 of 3)

- [Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes](https://mlsanity.com/paper/2408.03539/) (2024)
- [Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications](https://mlsanity.com/paper/2302.07549/) (2023)
- [A Review of Reinforcement Learning in Financial Applications](https://mlsanity.com/paper/2411.12746/) (2024)

## Built on by (most cited first, 1 of 1)

- [RL-ABC: Reinforcement Learning for Accelerator Beamline Control](https://mlsanity.com/paper/2604.19146/) (2026)

## Abstract (arXiv)

Particle accelerators play a pivotal role in advancing scientific research, yet optimizing beamline configurations to maximize particle transmission remains a labor-intensive task requiring expert intervention. In this work, we introduce RLABC (Reinforcement Learning for Accelerator Beamline Control), a Python-based library that reframes beamline optimization as a reinforcement learning (RL) problem. Leveraging the Elegant simulation framework, RLABC automates the creation of an RL environment from standard lattice and element input files, enabling sequential tuning of magnets to minimize particle losses. We define a comprehensive state representation capturing beam statistics, actions for adjusting magnet parameters, and a reward function focused on transmission efficiency. Employing the Deep Deterministic Policy Gradient (DDPG) algorithm, we demonstrate RLABC's efficacy on two beamlines, achieving transmission rates of 94% and 91%, comparable to expert manual optimizations. This approach bridges accelerator physics and machine learning, offering a versatile tool for physicists and RL researchers alike to streamline beamline tuning.