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RIS citation export for SUSPF038: Using Deep Reinforcement Learning for Designing Sub-Relativistic Electron Linac

TY - CONF
AU - Shin, Shin, S.W.
AU - Chai, J.-S.
AU - Ghergherehchi, M.
ED - Koscielniak, Shane
ED - Satogata, Todd
ED - Schaa, Volker RW
ED - Thomson, Jana
TI - Using Deep Reinforcement Learning for Designing Sub-Relativistic Electron Linac
J2 - Proc. of IPAC2018, Vancouver, BC, Canada, April 29-May 4, 2018
C1 - Vancouver, BC, Canada
T2 - International Particle Accelerator Conference
T3 - 9
LA - english
AB - Generally, when designing an accelerator device, the design is based on the experience and knowledge of the designer. Most of the design process proceeds by chang-ing the parameters and looking at the trends and then determining the optimal values. This process is time-consuming and tedious. In order to efficiently perform this tedious design process, a method using an optimization algorithm is used. Recently, many people started to get interested in the algorithm used in AlphaGo, which became famous when it won the professional Go player developed by google The algorithm used in AlphaGo is an algorithm called reinforcement learning that learns how to get optimal reward in various states by moving around a solution space that the agent has not told beforehand. In this paper, we will discuss about designing an particle accelerator by applying Deep Q-network algorithm which is one kind of deep learning reinforcement learning.
PB - JACoW Publishing
CP - Geneva, Switzerland
SP - 4720
EP - 4722
DA - 2018/06
PY - 2018
SN - 978-3-95450-184-7
DO - 10.18429/JACoW-IPAC2018-THPML032
UR - http://jacow.org/ipac2018/papers/thpml032.pdf
ER -