HeLyMARL uses virtual queues and sequential HAPPO updates to pace BS energy and user handover budgets within an episode, outperforming greedy and Lagrangian baselines in simulations.
Toward dynamic energy-efficient operation of cellular network infrastructure,
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Heterogeneous Multi-Agent Reinforcement Learning for Radio Resource Management under Coupled Finite-Horizon Constraints
HeLyMARL uses virtual queues and sequential HAPPO updates to pace BS energy and user handover budgets within an episode, outperforming greedy and Lagrangian baselines in simulations.