  Spatial Transformer Networks (STN) can generate geometric transformations
which modify input images to improve the classifier's performance. In this
work, we combine the idea of STN with Reinforcement Learning (RL). To this end,
we break the affine transformation down into a sequence of simple and discrete
transformations. We formulate the task as a Markovian Decision Process (MDP)
and use RL to solve this sequential decision-making problem. STN architectures
learn the transformation parameters by minimizing the classification error and
backpropagating the gradients through a sub-differentiable sampling module. In
our method, we are not bound to the differentiability of the sampling modules.
Moreover, we have freedom in designing the objective rather than only
minimizing the error; e.g., we can directly set the target as maximizing the
accuracy. We design multiple experiments to verify the effectiveness of our
method using cluttered MNIST and Fashion-MNIST datasets and show that our
method outperforms STN with a proper definition of MDP components.
