  We introduce reinforcement learning (RL) formulations of the problem of
finding the ground state of a many-body quantum mechanical model defined on a
lattice. We show that stoquastic Hamiltonians - those without a sign problem -
have a natural decomposition into stochastic dynamics and a potential
representing a reward function. The mapping to RL is developed for both
continuous and discrete time, based on a generalized Feynman-Kac formula in the
former case and a stochastic representation of the Schr\"odinger equation in
the latter. We discuss the application of this mapping to the neural
representation of quantum states, spelling out the advantages over approaches
based on direct representation of the wavefunction of the system.
