  This paper investigates the use of Reinforcement Learning for the robust
design of low-thrust interplanetary trajectories in presence of severe
disturbances, modeled alternatively as Gaussian additive process noise,
observation noise, control actuation errors on thrust magnitude and direction,
and possibly multiple missed thrust events. The optimal control problem is
recast as a time-discrete Markov Decision Process to comply with the standard
formulation of reinforcement learning. An open-source implementation of the
state-of-the-art algorithm Proximal Policy Optimization is adopted to carry out
the training process of a deep neural network, used to map the spacecraft
(observed) states to the optimal control policy. The resulting Guidance and
Control Network provides both a robust nominal trajectory and the associated
closed-loop guidance law. Numerical results are presented for a typical
Earth-Mars mission. First, in order to validate the proposed approach, the
solution found in a (deterministic) unperturbed scenario is compared with the
optimal one provided by an indirect technique. Then, the robustness and
optimality of the obtained closed-loop guidance laws is assessed by means of
Monte Carlo campaigns performed in the considered uncertain scenarios. These
preliminary results open up new horizons for the use of reinforcement learning
in the robust design of interplanetary missions.
