  Landing a quadrotor on an inclined surface is a challenging manoeuvre. The
final state of any inclined landing trajectory is not an equilibrium, which
precludes the use of most conventional control methods. We propose a deep
reinforcement learning approach to design an autonomous landing controller for
inclined surfaces. Using the proximal policy optimization (PPO) algorithm with
sparse rewards and a tailored curriculum learning approach, a robust policy can
be trained in simulation in less than 90 minutes on a standard laptop. The
policy then directly runs on a real Crazyflie 2.1 quadrotor and successfully
performs real inclined landings in a flying arena. A single policy evaluation
takes approximately 2.5 ms, which makes it suitable for a future embedded
implementation on the quadrotor.
