  We address the problem of active visual exploration of large 360{\deg}
inputs. In our setting an active agent with a limited camera bandwidth explores
its 360{\deg} environment by changing its viewing direction at limited discrete
time steps. As such, it observes the world as a sequence of narrow
field-of-view 'glimpses', deciding for itself where to look next. Our proposed
method exceeds previous works' performance by a significant margin without the
need for deep reinforcement learning or training separate networks as
sidekicks. A key component of our system are the spatial memory maps that make
the system aware of the glimpses' orientations (locations in the 360{\deg}
image). Further, we stress the advantages of retina-like glimpses when the
agent's sensor bandwidth and time-steps are limited. Finally, we use our
trained model to do classification of the whole scene using only the
information observed in the glimpses.
