  Accurate localization is a critical requirement for most robotic tasks. The
main body of existing work is focused on passive localization in which the
motions of the robot are assumed given, abstracting from their influence on
sampling informative observations. While recent work has shown the benefits of
learning motions to disambiguate the robot's poses, these methods are
restricted to granular discrete actions and directly depend on the size of the
global map. We propose Active Particle Filter Networks (APFN), an approach that
only relies on local information for both the likelihood evaluation as well as
the decision making. To do so, we couple differentiable particle filters with a
reinforcement learning agent that attends to the most relevant parts of the
map. The resulting approach inherits the computational benefits of particle
filters and can directly act in continuous action spaces while remaining fully
differentiable and thereby end-to-end optimizable as well as agnostic to the
input modality. We demonstrate the benefits of our approach with extensive
experiments in photorealistic indoor environments built from real-world 3D
scanned apartments. Videos and code are available at
http://apfn.cs.uni-freiburg.de.
