NeuroGraph-PPO: A GNN-Based Proximal Policy Optimization Framework for Autonomous Power Routing in Mars Interplanetary Grids
Year: 2025
Type: article
Abstract: This paper proposes a novel graph-reinforced learning approach forautonomous interplanetary power routing in Mars missions. We presentNeuroGraph-PPO, a hybrid framework combining Graph Neural Networks(GNN) and Proximal Policy Optimization (PPO) to optimize energy pathsin Martian microgrids. The model integrates outage forecasting, tariff prediction,load prioritization, and anomaly detection using machine learningand deep learning modules. A fully functional Python implementation isprovided and tested in simulated Mars-based grid environments.
Author: ZAIDI, ADNAN HAIDER
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6893292582
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