  Training a model-free deep reinforcement learning model to solve
image-to-image translation is difficult since it involves high-dimensional
continuous state and action spaces. In this paper, we draw inspiration from the
recent success of the maximum entropy reinforcement learning framework designed
for challenging continuous control problems to develop stochastic policies over
high dimensional continuous spaces including image representation, generation,
and control simultaneously. Central to this method is the Stochastic
Actor-Executor-Critic (SAEC) which is an off-policy actor-critic model with an
additional executor to generate realistic images. Specifically, the actor
focuses on the high-level representation and control policy by a stochastic
latent action, as well as explicitly directs the executor to generate low-level
actions to manipulate the state. Experiments on several image-to-image
translation tasks have demonstrated the effectiveness and robustness of the
proposed SAEC when facing high-dimensional continuous space problems.
