While deep reinforcement learning (RL) methods have achieved unprecedented
successes in a range of challenging problems, their applicability has been
mainly limited to simulation or game domains due to the high sample complexity
of the trial-and-error learning process. However, real-world robotic
applications often need a data-efficient learning process with safety-critical
constraints. In this paper, we consider the challenging problem of learning
unmanned aerial vehicle (UAV) control for tracking a moving target. To acquire
a strategy that combines perception and control, we represent the policy by a
convolutional neural network. We develop a hierarchical approach that combines
a model-free policy gradient method with a conventional feedback
proportional-integral-derivative (PID) controller to enable stable learning
without catastrophic failure. The neural network is trained by a combination of
supervised learning from raw images and reinforcement learning from games of
self-play. We show that the proposed approach can learn a target following
policy in a simulator efficiently and the learned behavior can be successfully
transferred to the DJI quadrotor platform for real-world UAV control