  We introduce \textit{Policy Guided Monte Carlo} (PGMC), a computational
framework using reinforcement learning to improve Markov chain Monte Carlo
(MCMC) sampling. The methodology is generally applicable, unbiased and opens up
a new path to automated discovery of efficient MCMC samplers. After developing
a general theory, we demonstrate some of PGMC's prospects on an Ising model on
the kagome lattice, including when the model is in its computationally
challenging kagome spin ice regime. Here, we show that PGMC is able to
automatically machine learn efficient MCMC updates without a priori knowledge
of the physics at hand.
