  Standard computer vision systems assume access to intelligently captured
inputs (e.g., photos from a human photographer), yet autonomously capturing
good observations is a major challenge in itself. We address the problem of
learning to look around: how can an agent learn to acquire informative visual
observations? We propose a reinforcement learning solution, where the agent is
rewarded for reducing its uncertainty about the unobserved portions of its
environment. Specifically, the agent is trained to select a short sequence of
glimpses after which it must infer the appearance of its full environment. To
address the challenge of sparse rewards, we further introduce sidekick policy
learning, which exploits the asymmetry in observability between training and
test time. The proposed methods learn observation policies that not only
perform the completion task for which they are trained, but also generalize to
exhibit useful "look-around" behavior for a range of active perception tasks.
