  In the past few years supervised and adversarial learning have been widely
adopted in various complex computer vision tasks. It seems natural to wonder
whether another branch of artificial intelligence, commonly known as
Reinforcement Learning (RL) can benefit such complex vision tasks. In this
study, we explore the plausible usage of RL in super resolution of remote
sensing imagery. Guided by recent advances in super resolution, we propose a
theoretical framework that leverages the benefits of supervised and
reinforcement learning. We argue that a straightforward implementation of RL is
not adequate to address ill-posed super resolution as the action variables are
not fully known. To tackle this issue, we propose to parameterize action
variables by matrices, and train our policy network using Monte-Carlo sampling.
We study the implications of parametric action space in a model-free
environment from theoretical and empirical perspective. Furthermore, we analyze
the quantitative and qualitative results on both remote sensing and non-remote
sensing datasets. Based on our experiments, we report considerable improvement
over state-of-the-art methods by encapsulating supervised models in a
reinforcement learning framework.
