| Title |
Using Deep Reinforcement Learning for Designing Sub-Relativistic Electron Linac |
| Authors |
- Shin, S.W. Shin, J.-S. Chai, M. Ghergherehchi
SKKU, Suwon, Republic of Korea
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| Abstract |
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.
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| Paper |
download THPML032.PDF [0.389 MB / 3 pages] |
| Export |
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| Conference |
IPAC2018, Vancouver, BC, Canada |
| Series |
International Particle Accelerator Conference (9th) |
| Proceedings |
Link to full IPAC2018 Proccedings |
| Session |
MC3/6/7 Poster Session |
| Date |
03-May-18 16:00–17:30 |
| Main Classification |
03 Novel Particle Sources and Acceleration Technologies |
| Sub Classification |
A15 New Acceleration Techniques (including DLA and THz) |
| Keywords |
network, electron, linac, cavity, acceleration |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editors |
Shane Koscielniak (TRIUMF, Vancouver, BC, Canada); Todd Satogata (JLab, Newport News, VA, USA); Volker RW Schaa (GSI, Darmstadt, Germany); Jana Thomson (TRIUMF, Vancouver, BC, Canada) |
| ISBN |
978-3-95450-184-7 |
| Published |
June 2018 |
| Copyright |
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