Physics instrument design with reinforcement learning
We present a case for the use of reinforcement learning (RL) for the design of physics instruments as an alternative to gradient-based instrument-optimization methods. Its applicability is demonstrated using two empirical studies. One is longitudinal segmentation of calorimeters and the second is bo...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/adf7ff |
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