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.
Learning optimal resource allocations in wireless systems,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.