prab | RL4AA Collaboration

Overview of the orbit correction method.

Orbit Correction Based on Improved Reinforcement Learning Algorithm

X. Chen, Y. Jia, X. Qi, Z. Wang, Y. He Chinese Academy of Sciences Physical Review Accelerators and Beams Abstract Recently, reinforcement learning (RL) algorithms have been applied to a wide range of control problems in accelerator commissioning. In order to achieve efficient and fast control, these algorithms need to be highly efficient, so as to minimize the online training time. In this paper, we incorporated the beam position monitor trend into the observation space of the twin delayed deep deterministic policy gradient (TD3) algorithm and trained two different structure agents, one based on physical prior knowledge and the other using the original TD3 network architecture....

Schema of the parameters’role within the learning loop.

Optimizing a superconducting radio-frequency gun using deep reinforcement learning

D. Meier1, L. V. Ramirez1, J. Völker1, J. Viefhaus1, B. Sick2, G. Hartmann1 1Helmholtz-Zentrum Berlin, 2University of Kassel Physical Review Accelerators and Beams Abstract Superconducting photoelectron injectors are promising for generating highly brilliant pulsed electron beams with high repetition rates and low emittances. Experiments such as ultrafast electron diffraction, experiments at the Terahertz scale, and energy recovery linac applications require such properties. However, optimizing the beam properties is challenging due to the high number of possible machine parameter combinations....

Schematic view of the GMPS control environment.

Real-time artificial intelligence for accelerator control: A study at the Fermilab Booster

J. St. John1, C. Herwig1, D. Kafkes1, J. Mitrevski1, W. A. Pellico1, G. N. Perdue1, A. Quintero-Parra1, B. A. Schupbach1, K. Seiya1, N. Tran1, M. Schram2, J. M. Duarte3, Y. Huang4, R. Keller5 1Fermi National Accelerator Laboratory, 2Thomas Jefferson National Accelerator Laboratory, 3University of California San Diego, 4Pacific Northwest National Laboratory, 5Columbia University Physical Review Accelerators and Beams Abstract We describe a method for precisely regulating the gradient magnet power supply (GMPS) at the Fermilab Booster accelerator complex using a neural network trained via reinforcement learning....

Plot of the reward received by the agent versus step number.

Policy gradient methods for free-electron laser and terahertz source optimization and stabilization at the FERMI free-electron laser at Elettra

F. H. O’Shea1, N. Bruchon2, G. Gaio1 1Elettra Sincrotrone Trieste, 2University of Trieste Physical Review Accelerators and Beams Abstract In this article we report on the application of a model-free reinforcement learning method to the optimization of accelerator systems. We simplify a policy gradient algorithm to accelerator control from sophisticated algorithms that have recently been demonstrated to solve complex dynamic problems. After outlining a theoretical basis for the functioning of the algorithm, we explore the small hyperparameter space to develop intuition about said parameters using a simple number-guess environment....

The RL paradigm as applied to particle accelerator control, showing the example of trajectory correction.

Sample-efficient reinforcement learning for CERN accelerator control

V. Kain1, S. Hirlander1, B. Goddard1, F. M. Velotti1, G. Z. Della Porta1, N. Bruchon2, G. Valentino3 1CERN, 2University of Trieste, 3University of Malta Physical Review Accelerators and Beams Abstract Numerical optimization algorithms are already established tools to increase and stabilize the performance of particle accelerators. These algorithms have many advantages, are available out of the box, and can be adapted to a wide range of optimization problems in accelerator operation....