  In recent years, unmanned aerial vehicle (UAV) related technology has
expanded knowledge in the area, bringing to light new problems and challenges
that require solutions. Furthermore, because the technology allows processes
usually carried out by people to be automated, it is in great demand in
industrial sectors. The automation of these vehicles has been addressed in the
literature, applying different machine learning strategies. Reinforcement
learning (RL) is an automation framework that is frequently used to train
autonomous agents. RL is a machine learning paradigm wherein an agent interacts
with an environment to solve a given task. However, learning autonomously can
be time consuming, computationally expensive, and may not be practical in
highly-complex scenarios. Interactive reinforcement learning allows an external
trainer to provide advice to an agent while it is learning a task. In this
study, we set out to teach an RL agent to control a drone using reward-shaping
and policy-shaping techniques simultaneously. Two simulated scenarios were
proposed for the training; one without obstacles and one with obstacles. We
also studied the influence of each technique. The results show that an agent
trained simultaneously with both techniques obtains a lower reward than an
agent trained using only a policy-based approach. Nevertheless, the agent
achieves lower execution times and less dispersion during training.
