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.
Interplanetary Low-Thrust Design Using Proximal Policy Optimization
2019 AAS/AIAA Astrodynamics Specialist Conference ; 2019 ; Portland, ME, United States
2019-08-11
Conference paper
No indication
English
Optimization of Low-Thrust Interplanetary Transfers
Online Contents | 2000
|Optimization of Low-Thrust Interplanetary Transfers
Springer Verlag | 2000
|Interplanetary maneuvers using radial thrust
Engineering Index Backfile | 1961
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