  Agile flights of autonomous quadrotors in cluttered environments require
constrained motion planning and control subject to translational and rotational
dynamics. Traditional model-based methods typically demand complicated design
and heavy computation. In this paper, we develop a novel deep reinforcement
learning-based method that tackles the challenging task of flying through a
dynamic narrow gate. We design a model predictive controller with its adaptive
tracking references parameterized by a deep neural network (DNN). These
references include the traversal time and the quadrotor SE(3) traversal pose
that encourage the robot to fly through the gate with maximum safety margins
from various initial conditions. To cope with the difficulty of training in
highly dynamic environments, we develop a reinforce-imitate learning framework
to train the DNN efficiently that generalizes well to diverse settings.
Furthermore, we propose a binary search algorithm that allows online adaption
of the SE(3) references to dynamic gates in real-time. Finally, through
extensive high-fidelity simulations, we show that our approach is robust to the
gate's velocity uncertainties and adaptive to different gate trajectories and
orientations.
