RL with constrained optimization can improve data freshness and fairness in delay-tolerant networks by explicitly modeling mission-relevant priorities and physical transmission constraints.
This paper develops a reinforcement learning framework for managing data bundles in deep-space communication networks where connectivity is intermittent and storage is limited.