  We examine the problem of learning mappings from state to state, suitable for
use in a model-based reinforcement-learning setting, that simultaneously
generalize to novel states and can capture stochastic transitions. We show that
currently popular generative adversarial networks struggle to learn these
stochastic transition models but a modification to their loss functions results
in a powerful learning algorithm for this class of problems.
