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

L5 · ResearcherResearcharXiv· 10/9/2026

Presents novel ML approach for solving constrained networking problems in extreme environments with intermittent connectivity.

AI Summary

Researchers propose a reinforcement learning framework using masked PPO for optimizing bundle management and flow control in deep-space communications networks.

Excerpt

Deep-space communications operate under intermittent connectivity, long propagation delays, and relay storage constraints. In such networks, the Bundle Protocol enables store-carry-forward communication, requiring relay nodes to manage limited storage and scarce contact opportunities. Relay-side scheduling and dropping decisions are inherently goal-oriented: transmission resources should be allocated to bundles most valuable to downstream mission objectives. Conventional schedulers, such as FIFO

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