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000478845 1001_ $$0P:(DE-H253)PIP1095111$$aKaiser, Jan$$b0$$eCorresponding author
000478845 1112_ $$a39th International Conference on Machine Learning$$cBaltimore$$d2022-07-17 - 2022-07-23$$gICML 2022$$wUSA
000478845 245__ $$aLearning-based Optimisation of Particle Accelerators Under Partial Observability Without Real-World Training
000478845 260__ $$bPMLR$$c2022
000478845 29510 $$aProceedings of the 39th International Conference on Machine Learning
000478845 300__ $$a10575-10585
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000478845 520__ $$aIn recent work, it has been shown that reinforcement learning (RL) is capable of solving a variety of problems at sometimes super-human performance levels. But despite continued advances in the field, applying RL to complex real-world control and optimisation problems has proven difficult. In this contribution, we demonstrate how to successfully apply RL to the optimisation of a highly complex real-world machine – specifically a linear particle accelerator – in an only partially observable setting and without requiring training on the real machine. Our method outperforms conventional optimisation algorithms in both the achieved result and time taken as well as already achieving close to human-level performance. We expect that such automation of machine optimisation will push the limits of operability, increase machine availability and lead to a paradigm shift in how such machines are operated, ultimately facilitating advances in a variety of fields, such as science and medicine among many others.
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000478845 7001_ $$0P:(DE-H253)PIP1014315$$aStein, Oliver$$b1
000478845 7001_ $$0P:(DE-H253)PIP1087213$$aEichler, Annika$$b2
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