Conveners
Contributed talks: Student/Junior Talks
- Joseph Wolfenden (University of Liverpool)
Contributed talks: RL Applied to Particle Accelerators
- Simon Hirlaender (PLUS University Salzburg)
Contributed talks: RL Applied to Other Systems
- Alexander Brynes (Science & Technology Facilities Council)
Contributed talks: RL Applied to Other Systems and Others
- Amelia Pollard (ASTeC)
Industrial energy management presents a challenging control problem characterized by strict safety hierarchies, stochastic load fluctuations, and setting actions often has a significantly delayed effects. This work investigates a Reliable Hierarchical Control Architecture composed of probabilistic forecasting paired with a decision-maker.
The processed problem is characterized by an...
Stripper foil degradation in the Low Energy Ion Ring (LEIR) causes beam distribution drift that progressively degrades performance during multi-turn stacking at flat bottom. World models have emerged as a promising approach for sample-efficient and robust agents, enabling them to improve their behavior by rolling out policies in learned environment models between real interactions, thereby...
Using domain knowledge to improve deep RL policies is a current challenge. LEGIBLE mines rules from an RL policy, constituting a partially symbolic representation. These rules describe which decisions the RL policy makes and which it avoids making. It then generalizes the mined rules using domain knowledge. Finally, it evaluates generalized rules to determine which generalizations improve...
Designing advanced particle-physics instruments requires navigating a high-dimensional space of discrete and continuous choices while satisfying strict constraints on material, cost, and geometry. In practice, these constraints evolve throughout an experiment’s lifetime, making it insufficient to optimize a single “best” detector configuration. We present a resource-conditioned reinforcement...
Commissioning slow extracted beams from the CERN Super Proton Synchrotron (SPS) to the North Area experimental targets requires trajectory control through multiple transfer lines using corrector magnets—a process that traditionally demands significant expert intervention. Previous work demonstrated using reinforcement learning (RL) for automated trajectory correction based on secondary...
Precision orbit correction is critical for maintaining beam stability and transmission efficiency in low-energy beam transport (LEBT) systems, particularly under the influence of nonlinear field effects and strong space-charge forces. Traditional correction techniques often struggle to cope with such complexities, especially when real-time responsiveness is required.
In this work, we present...
The operation of solid, cryogenic polarized targets in nuclear physics experiments relies on continuous tuning of the microwave frequency to compensate for radiation damage and evolving material properties, a task that is traditionally performed through manual trial-and-error by expert operators. This work presents a data driven control framework that combines surrogate modeling with...
Experimental studies of beauty hadron decays face significant challenges due to a wide range of backgrounds arising from the numerous possible decay channels with similar final states. For a particular signal decay, the process for ascertaining the most relevant background processes necessitates a detailed analysis of final state particles, potential misidentifications, and kinematic overlaps,...
Multi-Agent Reinforcement Learning (MARL) is an important subfield of Reinforcement Learning, in which multiple agents learn in a shared environment. The simultaneous learning of several players naturally arises in domains like robotics, network communication and traffic control, where agents affect and influence one another. Thus, MARL can simulate real-world problems in a reliable way, and...
This talk presents an overview of neural network deployment on reconfigurable hardware, with a particular focus on modern AMD FPGA platforms and the Versal Adaptive Compute Acceleration Platform (ACAP). The discussion begins with examples from physics applications where reinforcement learning and recurrent neural networks are jointly employed for real-time control and decision-making.
An...
This study introduces a novel binary trigger-based state representation for deep reinforcement learning (DRL) in stock trading. Unlike conventional approaches using continuous technical indicators (MACD, RSI, CCI, ADX), we encode market state via binary signals: MVX (moving-average crossover) and BOLLX (Bollinger band breakout). We also propose trigger-date filtering, which trains only on...
We present an RL-based approach for optimizing beam injection into the Alternating Gradient Synchrotron (AGS) using a fully differentiable in silico environment. Beam diagnostics from multiwire screens in the BtA transfer line and turn-by-turn beam position monitors throughout the AGS lattice are leveraged to characterize injection quality, quantify beam survival time, and localize beam losses...
