  We introduce a method by which a generative model learning the joint
distribution between actions and future states can be used to automatically
infer a control scheme for any desired reward function, which may be altered on
the fly without retraining the model. In this method, the problem of action
selection is reduced to one of gradient descent on the latent space of the
generative model, with the model itself providing the means of evaluating
outcomes and finding the gradient, much like how the reward network in Deep
Q-Networks (DQN) provides gradient information for the action generator. Unlike
DQN or Actor-Critic, which are conditional models for a specific reward, using
a generative model of the full joint distribution permits the reward to be
changed on the fly. In addition, the generated futures can be inspected to gain
insight in to what the network 'thinks' will happen, and to what went wrong
when the outcomes deviate from prediction.
