Comparative Analysis of Reinforcement Learning Algorithms for Robust Interplanetary Trajectory Design

This paper focuses on the application of reinforcement learning to the robust design of low-thrust interplanetary trajectories in presence of severe dynamical uncertainties modeled as Gaussian additive process noise. A closed-loop control policy is used to steer the spacecraft to a final target state despite the perturbations. The control policy is approximated by a deep neural network, trained by reinforcement learning to output the optimal control thrust given as input the current spacecraft state. The effectiveness of three different model-free reinforcement learning algorithms is assessed and compared on a three-dimensional low-thrust transfer between Earth and Mars elected as study case.