This paper aims to demonstrate a reinforcement learning technique for developing complex, decision-making policies capable of planning interplanetary transfers.Using Proximal Policy Optimization (PPO), a neural network agent is trained to produce a closed-loop controller capable of transfers between Earth and Mars.The agent is trained in an environment that utilizes a medium fidelity solar electric propulsion model and a real ephemeris model of the Earth and Mars. The results are compared against those generated by the Evolutionary Mission Trajectory Generator (EMTG) tool.


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    Title :

    Interplanetary Low-Thrust Design Using Proximal Policy Optimization


    Contributors:

    Conference:

    2019 AAS/AIAA Astrodynamics Specialist Conference ; 2019 ; Portland, ME, United States


    Publication date :

    2019-08-11


    Type of media :

    Conference paper


    Type of material :

    No indication


    Language :

    English




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