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.
User association for load balancing in heterogeneous cellular networks,
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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.