Learning to Explore by Reinforcement over High-Level Options
  Autonomous 3D environment exploration is a fundamental task for various
applications such as navigation. The goal of exploration is to investigate a
new environment and build its occupancy map efficiently. In this paper, we
propose a new method which grants an agent two intertwined options of
behaviors: "look-around" and "frontier navigation". This is implemented by an
option-critic architecture and trained by reinforcement learning algorithms. In
each timestep, an agent produces an option and a corresponding action according
to the policy. We also take advantage of macro-actions by incorporating classic
path-planning techniques to increase training efficiency. We demonstrate the
effectiveness of the proposed method on two publicly available 3D environment
datasets and the results show our method achieves higher coverage than
competing techniques with better efficiency.
