  With the increase in demands for service robots and automated inspection,
agents need to localize in its surrounding environment to achieve more natural
communication with humans by shared contexts. In this work, we propose a novel
but straightforward task of precise target view localization for look around
agents called the FindView task. This task imitates the movements of PTZ
cameras or user interfaces for 360 degree mediums, where the observer must
"look around" to find a view that exactly matches the target. To solve this
task, we introduce a rule-based agent that heuristically finds the optimal view
and a policy learning agent that employs reinforcement learning to learn by
interacting with the 360 degree scene. Through extensive evaluations and
benchmarks, we conclude that learned methods have many advantages, in
particular precise localization that is robust to corruption and can be easily
deployed in novel scenes.
