A. Ivanov, I. Agapov, A. Eichler, S. Tomin
Deutsches Elektronen Synchrotron DESY
12th International Particle Accelerator Conference
Abstract
We propose an approach for incorporating acceleratorphysics models into reinforcement learning agents. The proposed approach is based on the Taylor mapping technique for the simulation of particle dynamics. The resulting computational graph is represented as a polynomial neural network and embedded into the traditional reinforcement learning agents. The application of the model is demonstrated in a nonlinear simulation model of beam transmission. The comparison of the approach with the traditional numerical optimization as well as neural networks-based agents demonstrates better convergence of the proposed technique.
Read the paper: https://jacow.org/ipac2021/papers/thpab191.pdf
Contact: Andrei Ivanov