  We present a reinforcement learning approach for detecting objects within an
image. Our approach performs a step-wise deformation of a bounding box with the
goal of tightly framing the object. It uses a hierarchical tree-like
representation of predefined region candidates, which the agent can zoom in on.
This reduces the number of region candidates that must be evaluated so that the
agent can afford to compute new feature maps before each step to enhance
detection quality. We compare an approach that is based purely on zoom actions
with one that is extended by a second refinement stage to fine-tune the
bounding box after each zoom step. We also improve the fitting ability by
allowing for different aspect ratios of the bounding box. Finally, we propose
different reward functions to lead to a better guidance of the agent while
following its search trajectories. Experiments indicate that each of these
extensions leads to more correct detections. The best performing approach
comprises a zoom stage and a refinement stage, uses aspect-ratio modifying
actions and is trained using a combination of three different reward metrics.
