  Inspection and maintenance are two crucial aspects of industrial pipeline
plants. While robotics has made tremendous progress in the mechanic design of
in-pipe inspection robots, the autonomous control of such robots is still a big
open challenge due to the high number of actuators and the complex manoeuvres
required. To address this problem, we investigate the usage of Deep
Reinforcement Learning for achieving autonomous navigation of in-pipe robots in
pipeline networks with complex topologies. Moreover, we introduce a
hierarchical policy decomposition based on Hierarchical Reinforcement Learning
to learn robust high-level navigation skills. We show that the hierarchical
structure introduced in the policy is fundamental for solving the navigation
task through pipes and necessary for achieving navigation performances superior
to human-level control.
