Exploring the flavor structure of quarks and leptons with reinforcement learning. (arXiv:2304.14176v3 [hep-ph] UPDATED)

We propose a method to explore the flavor structure of quarks and leptons
with reinforcement learning. As a concrete model, we utilize a basic
value-based algorithm for models with $U(1)$ flavor symmetry. By training
neural networks on the $U(1)$ charges of quarks and leptons, the agent finds 21
models to be consistent with experimentally measured masses and mixing angles
of quarks and leptons. In particular, an intrinsic value of normal ordering
tends to be larger than that of inverted ordering, …

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