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In recent years, unmanned aerial vehicle (UAV) related technology hasexpanded knowledge in the area, bringing to light new problems and challengesthat require solutions. Furthermore, because the technology allows processesusually carried out by people to be automated, it is in great demand inindustrial sectors. The automation of these vehicles has been addressed in theliterature, applying different machine learning strategies. Reinforcementlearning (RL) is an automation framework that is frequently used to trainautonomous agents. RL is a machine learning paradigm wherein an agent interactswith an environment to solve a given task. However, learning autonomously canbe time consuming, computationally expensive, and may not be practical inhighly-complex scenarios. Interactive reinforcement learning allows an externaltrainer to provide advice to an agent while it is learning a task. In thisstudy, we set out to teach an RL agent to control a drone using reward-shapingand policy-shaping techniques simultaneously. Two simulated scenarios wereproposed for the training; one without obstacles and one with obstacles. Wealso studied the influence of each technique. The results show that an agenttrained simultaneously with both techniques obtains a lower reward than anagent trained using only a policy-based approach. Nevertheless, the agentachieves lower execution times and less dispersion during training.

Reinforcement Learning for UAV control with Policy and Reward Shaping