  Tensor network codes enable structured construction and manipulation of
stabilizer codes out of small seed codes. Here, we apply reinforcement learning
to tensor network code geometries and demonstrate how optimal stabilizer codes
can be found. Using the projective simulation framework, our reinforcement
learning agent consistently finds the best possible codes given an environment
and set of allowed actions, including for codes with more than one logical
qubit. The agent also consistently outperforms a random search, for example
finding an optimal code with a $10\%$ frequency after 1000 trials, vs a
theoretical $0.16\%$ from random search, an improvement by a factor of 65.
