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This paper investigates the use of Reinforcement Learning for the robustdesign of low-thrust interplanetary trajectories in presence of severedisturbances, 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 isrecast as a time-discrete Markov Decision Process to comply with the standardformulation of reinforcement learning. An open-source implementation of thestate-of-the-art algorithm Proximal Policy Optimization is adopted to carry outthe training process of a deep neural network, used to map the spacecraft(observed) states to the optimal control policy. The resulting Guidance andControl Network provides both a robust nominal trajectory and the associatedclosed-loop guidance law. Numerical results are presented for a typicalEarth-Mars mission. First, in order to validate the proposed approach, thesolution found in a (deterministic) unperturbed scenario is compared with theoptimal one provided by an indirect technique. Then, the robustness andoptimality of the obtained closed-loop guidance laws is assessed by means ofMonte Carlo campaigns performed in the considered uncertain scenarios. Thesepreliminary results open up new horizons for the use of reinforcement learningin the robust design of interplanetary missions.

Reinforcement Learning for Low-Thrust Trajectory Design of Interplanetary Missions