Speaker
Description
We present the first live deployment of reinforcement learning for closed-loop control of an operational cyclotron, demonstrated at the PSI Injector-2 during a 12-day beam-development campaign. The agent controlled multiple magnetic and RF actuators using phase, loss, and current diagnostics to minimize phase deviations and radial losses directly on the machine.
For a fixed operating configuration, real-machine training converged within a few hours, and surrogate-based pretraining reduced online training time substantially. The campaign also showed that policies are highly configuration-specific: transfer between nearby turn numbers was limited, consistent with measured changes in machine sensitivity. Despite this, the agent restored final beam phases to within about ±1° of target, significantly reduced losses, and maintained safe operation under interlock-aware training. Overnight evaluation further demonstrated drift compensation and recovery from deliberate perturbations. The results provide a first validation of RL-assisted tuning for cyclotron operation and motivate future work on safer transfer and multi-configuration learning.
