@inproceedings{65f29ef8e8194fa8b1f0627a5d50ca82,
title = "Comparative Analysis of Reinforcement Learning Algorithms for Robust Interplanetary Trajectory Design",
abstract = "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.",
keywords = "Reinforcement learning, Robust trajectory design, Space trajectory optimization, Spacecraft guidance",
author = "Lorenzo Federici and Alessandro Zavoli and Roberto Furfaro",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 2nd International Conference on Applied Intelligence and Informatics , AII 2022 ; Conference date: 01-09-2022 Through 03-09-2022",
year = "2023",
doi = "10.1007/978-3-031-25755-1_9",
language = "English (US)",
isbn = "9783031257544",
series = "Studies in Computational Intelligence",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "133--149",
editor = "Cosimo Ieracitano and Nadia Mammone and {Di Clemente}, Marco and Mufti Mahmud and Roberto Furfaro and Morabito, {Francesco Carlo}",
booktitle = "The Use of Artificial Intelligence for Space Applications - Workshop at the 2022 International Conference on Applied Intelligence and Informatics",
address = "Germany",
}