  With the long-term goal of reducing the image processing time on an
autonomous mobile robot in mind we explore in this paper the use of log-polar
like image data with gaze control. The gaze control is not done on the
Cartesian image but on the log-polar like image data. For this we start out
from the classic deep reinforcement learning approach for Atari games. We
extend an A3C deep RL approach with an LSTM network, and we learn the policy
for playing three Atari games and a policy for gaze control. While the Atari
games already use low-resolution images of 80 by 80 pixels, we are able to
further reduce the amount of image pixels by a factor of 5 without losing any
gaming performance.
