  In this paper, we apply reinforcement learning to particle physics model
building. As an example environment, we use the space of Froggatt-Nielsen type
models for quark masses. Using a basic policy-based algorithm we show that
neural networks can be successfully trained to construct Froggatt-Nielsen
models which are consistent with the observed quark masses and mixing. The
trained policy networks lead from random to phenomenologically acceptable
models for over 90% of episodes and after an average episode length of about 20
steps. We also show that the networks are capable of finding models proposed in
the literature when starting at nearby configurations.
