This paper investigates the use of reinforcement learning for the fuel-optimal guidance of a spacecraft during a time-free low-thrust transfer between two libration point orbits in the cislunar environment. To this aim, a deep neural network is trained via proximal policy optimization to map any spacecraft state to the optimal control action. A general-purpose reward is used to guide the network toward a fuel-optimal control law, regardless of the specific pair of libration orbits considered and without the use of any ad hoc reward shaping technique. Eventually, the learned control policies are compared with the optimal solutions provided by a direct method in two different mission scenarios, and Monte Carlo simulations are used to assess the policies’ robustness to navigation uncertainties.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Autonomous Guidance Between Quasiperiodic Orbits in Cislunar Space via Deep Reinforcement Learning


    Contributors:

    Published in:

    Publication date :

    2023-08-24


    Size :

    12 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Options for staging orbits in cislunar space

    Whitley, Ryan / Martinez, Roland | IEEE | 2016


    Cislunar Space Situational Awareness Sensor Tasking using Deep Reinforcement Learning Agents

    Siew, Peng Mun | British Library Conference Proceedings | 2022


    Options for Staging Orbits in Cislunar Space

    Whitley, Ryan / Martinez, Roland | NTRS | 2016


    Options for Staging Orbits in Cislunar Space

    R. Whitley / R. Martinez | NTIS | 2016