We propose a method to explore the flavor structure of quarks and leptons with reinforcement learning. As a concrete model, we utilize a basic policy-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, and the normal ordering is well fitted with the current experimental data in contrast to the inverted ordering. A specific value of effective mass for the neutrinoless double beta decay and a sizable leptonic CP violation induced by an angular component of flavon field are predicted by autonomous behavior of the agent.

翻译：我们提出一种利用强化学习探索夸克与轻子味结构的方法。作为具体模型，我们采用基于策略的基本算法来处理具有$U(1)$味对称性的模型。通过训练神经网络处理夸克与轻子的$U(1)$荷，该智能体找到了21个与实验测量的夸克和轻子质量及混合角相一致的模型。特别地，正排序的内在值倾向于大于反排序的内在值，且正排序与当前实验数据的拟合度优于反排序。智能体的自主行为预测了无中微子双β衰变的有效质量的具体数值，以及由 flavon 场角分量诱发的显著轻子 CP 破坏。