  In many robotic tasks, such as autonomous drone racing, the goal is to travel
through a set of waypoints as fast as possible. A key challenge for this task
is planning the time-optimal trajectory, which is typically solved by assuming
perfect knowledge of the waypoints to pass in advance. The resulting solution
is either highly specialized for a single-track layout, or suboptimal due to
simplifying assumptions about the platform dynamics. In this work, a new
approach to near-time-optimal trajectory generation for quadrotors is
presented. Leveraging deep reinforcement learning and relative gate
observations, our approach can compute near-time-optimal trajectories and adapt
the trajectory to environment changes. Our method exhibits computational
advantages over approaches based on trajectory optimization for non-trivial
track configurations. The proposed approach is evaluated on a set of race
tracks in simulation and the real world, achieving speeds of up to 60 km/h with
a physical quadrotor.
