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About Deep Reinforcement Learning Meets Graph Neural Networks: Exploring A Routing Optimization Use Case
The document discusses integrating graph neural networks (GNNs) into deep reinforcement learning (DRL) agents to enable DRL solutions for network optimization to generalize to unseen network topologies. Specifically, it explores using this approach for a routing optimization problem in optical networks. Evaluation on 180 unseen synthetic networks and 232 real-world networks shows the DRL+GNN agent can outperform existing solutions on networks not seen during training, demonstrating improved generalization capabilities.
Detailed Information
| Author: | ['Alain Gómez Cabrera'] |
|---|---|
| Publication Year: | 2024 |
| Pages: | 11 |
| Language: | English |
| Format: | |
| Price: | FREE |
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