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arXiv Machine Learning By Sajjad Baghaeea, Aimin Lib, Shanmugarajan Balasubramanian, Elif Uysal

Goal-Oriented Bundle Management and Flow Control for Deep-Space Communications via Reinforcement Learning

Read the original on arXiv Machine Learning →

The paper introduces a reinforcement‑learning framework for managing bundles and flow control in deep‑space networks, where relay nodes must schedule and drop data under intermittent connectivity and limited storage. It defines a goal‑oriented loss based on excess age to prioritize mission‑relevant information and formulates relay control as a constrained semi‑Markov decision process solved with masked Lagrangian PPO. Simulations show that the approach improves age‑based utility and inter‑flow fairness compared to traditional schedulers.

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