  Active localization is the problem of generating robot actions that allow it
to maximally disambiguate its pose within a reference map. Traditional
approaches to this use an information-theoretic criterion for action selection
and hand-crafted perceptual models. In this work we propose an end-to-end
differentiable method for learning to take informative actions that is
trainable entirely in simulation and then transferable to real robot hardware
with zero refinement. The system is composed of two modules: a convolutional
neural network for perception, and a deep reinforcement learned planning
module. We introduce a multi-scale approach to the learned perceptual model
since the accuracy needed to perform action selection with reinforcement
learning is much less than the accuracy needed for robot control. We
demonstrate that the resulting system outperforms using the traditional
approach for either perception or planning. We also demonstrate our approaches
robustness to different map configurations and other nuisance parameters
through the use of domain randomization in training. The code is also
compatible with the OpenAI gym framework, as well as the Gazebo simulator.
