  Aerial robots are increasingly being utilized for environmental monitoring
and exploration. However, a key challenge is efficiently planning paths to
maximize the information value of acquired data as an initially unknown
environment is explored. To address this, we propose a new approach for
informative path planning based on deep reinforcement learning (RL). Combining
recent advances in RL and robotic applications, our method combines tree search
with an offline-learned neural network predicting informative sensing actions.
We introduce several components making our approach applicable for robotic
tasks with high-dimensional state and large action spaces. By deploying the
trained network during a mission, our method enables sample-efficient online
replanning on platforms with limited computational resources. Simulations show
that our approach performs on par with existing methods while reducing runtime
by 8-10x. We validate its performance using real-world surface temperature
data.
