  In apprenticeship learning (AL), agents learn by watching or acquiring human
demonstrations on some tasks of interest. However, the lack of human
demonstrations in novel tasks where they may not be a human expert yet, or when
it is too expensive and/or time consuming to acquire human demonstrations
motivated a new algorithm: Apprenticeship bootstrapping (ABS). The basic idea
is to learn from demonstrations on sub-tasks then autonomously bootstrap a
model on the main, more complex, task. The original ABS used inverse
reinforcement learning (ABS-IRL). However, the approach is not suitable for
continuous action spaces.
  In this paper, we propose ABS via Deep learning (ABS-DL). It is first
validated in a simulation environment on an aerial and ground coordination
scenario, where an Unmanned Aerial Vehicle (UAV) is required to maintain three
Unmanned Ground Vehicles (UGVs) within a field of view of the UAV 's camera
(FoV). Moving a machine learning algorithm from a simulation environment to an
actual physical platform is challenging because `mistakes' made by the
algorithm while learning could lead to the damage of the platform. We then take
this extra step to test the algorithm in a physical environment. We propose a
safety-net as a protection layer to ensure that the autonomy of the algorithm
in learning does not compromise the safety of the platform. The tests of ABS-DL
in the real environment can guarantee a damage-free, collision avoidance
behaviour of autonomous bodies. The results show that performance of the
proposed approach is comparable to that of a human, and competitive to the
traditional approach using expert demonstrations performed on the composite
task. The proposed safety-net approach demonstrates its advantages when it
enables the UAV to operate more safely under the control of the ABS-DL
algorithm.
