  We propose an approach to learning agents for active robotic mapping, where
the goal is to map the environment as quickly as possible. The agent learns to
map efficiently in simulated environments by receiving rewards corresponding to
how fast it constructs an accurate map. In contrast to prior work, this
approach learns an exploration policy based on a user-specified prior over
environment configurations and sensor model, allowing it to specialize to the
specifications. We evaluate the approach through a simulated Disaster Mapping
scenario and find that it achieves performance slightly better than a
near-optimal myopic exploration scheme, suggesting that it could be useful in
more complicated problem scenarios.
