Comparative Analysis of Reinforcement Learning Algorithms for Robust Interplanetary Trajectory Design

Lorenzo Federici, Alessandro Zavoli, Roberto Furfaro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationThe Use of Artificial Intelligence for Space Applications - Workshop at the 2022 International Conference on Applied Intelligence and Informatics
EditorsCosimo Ieracitano, Nadia Mammone, Marco Di Clemente, Mufti Mahmud, Roberto Furfaro, Francesco Carlo Morabito
PublisherSpringer Science and Business Media Deutschland GmbH
Pages133-149
Number of pages17
ISBN (Print)9783031257544
DOIs
StatePublished - 2023
Event2nd International Conference on Applied Intelligence and Informatics , AII 2022 - Reggio Calabria, Italy
Duration: Sep 1 2022Sep 3 2022

Publication series

NameStudies in Computational Intelligence
Volume1088
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Conference

Conference2nd International Conference on Applied Intelligence and Informatics , AII 2022
Country/TerritoryItaly
CityReggio Calabria
Period9/1/229/3/22

Keywords

  • Reinforcement learning
  • Robust trajectory design
  • Space trajectory optimization
  • Spacecraft guidance

ASJC Scopus subject areas

  • Artificial Intelligence

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