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We describe an approach to learning optimal control policies for a large, linear particle accelerator using deep reinforcement learning coupled with a high-fidelity physics engine.
Asynchronous methods for deep reinforcement learning
V. Mnih et al · 1937
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Model-independent particle accelerator tuning
A. Scheinker et al · 2013
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Multi-objective particle swarm and genetic algorithm for the optimization of the lansce linac operation
X. Pang and L.J. Rybarcyk · 2014
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Trust region policy optimization
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Neural networks for modeling and control of particle accelerators
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https://github.com/apphys/hpsim
HPSim
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https://gym.openai.com/
OpenAI Gym
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Proximal policy optimization algorithm
J. Schulman et al
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Guided policy search
S. Levine and V. Koltun
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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Policy gradients
Sergey Levine · 2017
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