  In the adaptive information gathering problem, a policy is required to select
an informative sensing location using the history of measurements acquired thus
far. While there is an extensive amount of prior work investigating effective
practical approximations using variants of Shannon's entropy, the efficacy of
such policies heavily depends on the geometric distribution of objects in the
world. On the other hand, the principled approach of employing online POMDP
solvers is rendered impractical by the need to explicitly sample online from a
posterior distribution of world maps.
  We present a novel data-driven imitation learning framework to efficiently
train information gathering policies. The policy imitates a clairvoyant oracle
- an oracle that at train time has full knowledge about the world map and can
compute maximally informative sensing locations. We analyze the learnt policy
by showing that offline imitation of a clairvoyant oracle is implicitly
equivalent to online oracle execution in conjunction with posterior sampling.
This observation allows us to obtain powerful near-optimality guarantees for
information gathering problems possessing an adaptive sub-modularity property.
As demonstrated on a spectrum of 2D and 3D exploration problems, the trained
policies enjoy the best of both worlds - they adapt to different world map
distributions while being computationally inexpensive to evaluate.
