  Deep Reinforcement Learning (DRL) has become a prominent paradigm to design
trajectories for autonomous unmanned aerial vehicles (UAV) used as flying
access points in the context of cellular or Internet of Things (IoT)
connectivity. However, the prohibitively high training data demand severely
restricts the applicability of RL-based trajectory planning in real-world
missions. We propose a model-aided deep Q-learning approach that, in contrast
to previous work, requires a minimum of expensive training data samples and is
able to guide a flight-time restricted UAV on a data harvesting mission without
prior knowledge of wireless channel characteristics and limited knowledge of
wireless node locations. By exploiting some known reference wireless node
positions and channel gain measurements, we seek to learn a model of the
environment by estimating unknown node positions and learning the wireless
channel characteristics. Interaction with the model allows us to train a deep
Q-network (DQN) to approximate the optimal UAV control policy. We show that in
comparison with standard DRL approaches, the proposed model-aided approach
requires at least one order of magnitude less training data samples to reach
identical data collection performance, hence offering a first step towards
making DRL a viable solution to the problem.
