Author: Caliari, C.
Paper Title Page
WEBO04 Enhancement of the S-DALINAC Control System with Machine Learning Methods 473
  • J.H. Hanten, M. Arnold, J. Birkhan, C. Caliari, N. Pietralla, M. Steinhorst
    TU Darmstadt, Darmstadt, Germany
  Funding: *Work supported by DFG through GRK 2128
For the EPICS-based control system of the superconducting Darmstadt electron linear accelerator S-DALINAC**, supporting infrastructures based on machine learning are currently developed. The most important support for the operators is to assist the beam setup and controlling with reinforcement learning using artificial neural networks. A particle accelerator has a very large parameter space with often hidden relationships between them. Therefore neural networks are a suited instrument to use for approximating the needed value function which represents the value of a certain action in a certain state. Different neural network structures and their training with reinforcement learning are currently tested with simulations. Also there are different candidates for the reinforcement learning algorithms such as Deep-Q-Networks (DQN) or Deep-Deterministic-Policy-Gradient (DDPG). In this contribution the concept and first results will be presented.
**N. Pietralla, Nuclear Physics News, Vol. 28, No.2, 4 (2018)
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About • paper received ※ 03 September 2019       paper accepted ※ 09 September 2019       issue date ※ 10 November 2019  
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