  Deep Reinforcement Learning (DRL) has shown impressive performance on domains
with visual inputs, in particular various games. However, the agent is usually
trained on a fixed environment, e.g. a fixed number of levels. A growing mass
of evidence suggests that these trained models fail to generalize to even
slight variations of the environments they were trained on. This paper advances
the hypothesis that the lack of generalization is partly due to the input
representation, and explores how rotation, cropping and translation could
increase generality. We show that a cropped, translated and rotated observation
can get better generalization on unseen levels of two-dimensional arcade games
from the GVGAI framework. The generality of the agents is evaluated on both
human-designed and procedurally generated levels.
