Multi-agent deep RL enables distributed link scheduling that improves both average and 5th percentile throughput, approaching centralized performance and remaining robust to network density changes.
An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel,
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When Multiple Agents Learn to Schedule: A Distributed Radio Resource Management Framework
Multi-agent deep RL enables distributed link scheduling that improves both average and 5th percentile throughput, approaching centralized performance and remaining robust to network density changes.