Actor-critic reinforcement learning approach to relative motion guidance in near-rectilinear orbit

Andrea Scorsoglio, Roberto Furfaro, Richard Linares, Mauro Massari

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

28 Scopus citations


This paper aims a developing a new feedback guidance algorithm for docking maneuvers in the cislunar environment. In particular, the goal is to create an algorithm that is lightweight, closed-loop and capable of taking path constraints into account. The problem has been solved starting from the well know Zero-Effort-Miss/Zero-Effort-Velocity (ZEM/ZEV) guidance using machine learning to improve its capabilities and widen its field of application. The algorithm has been developed in the circular restricted three body problem (CRTBP) framework for Near Rectilinear Orbits (NRO) in the Earth-Moon system but the results can be easily generalized to many more guidance problems. The results are satisfactory and show that reinforcement learning can be effectively used to solve constrained relative spacecraft guidance problems.

Original languageEnglish (US)
Title of host publicationSpaceflight Mechanics 2019
EditorsFrancesco Topputo, Andrew J. Sinclair, Matthew P. Wilkins, Renato Zanetti
PublisherUnivelt Inc.
Number of pages20
ISBN (Print)9780877036593
StatePublished - 2019
Event29th AAS/AIAA Space Flight Mechanics Meeting, 2019 - Maui, United States
Duration: Jan 13 2019Jan 17 2019

Publication series

NameAdvances in the Astronautical Sciences
ISSN (Print)0065-3438


Conference29th AAS/AIAA Space Flight Mechanics Meeting, 2019
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Aerospace Engineering
  • Space and Planetary Science


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