Spatial-Temporal Reinforcement Learning for Network Routing with Non-Markovian Traffic
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866918108358246400 |
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| author | Wang, Molly Leung, Kin. K |
| author_facet | Wang, Molly Leung, Kin. K |
| contents | Reinforcement Learning (RL) has been widely used for packet routing in communication networks, but traditional RL methods rely on the Markov assumption that the current state contains all necessary information for decision-making. In reality, internet traffic is non-Markovian, and past states do influence routing performance. Moreover, common deep RL approaches use function approximators, such as neural networks, that do not model the spatial structure in network topologies. To address these shortcomings, we design a network environment with non-Markovian traffic and introduce a spatial-temporal RL (STRL) framework for packet routing. Our approach outperforms traditional baselines by more than 19% during training and 7% for inference despite a change in network topology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_22174 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Spatial-Temporal Reinforcement Learning for Network Routing with Non-Markovian Traffic Wang, Molly Leung, Kin. K Machine Learning Artificial Intelligence Reinforcement Learning (RL) has been widely used for packet routing in communication networks, but traditional RL methods rely on the Markov assumption that the current state contains all necessary information for decision-making. In reality, internet traffic is non-Markovian, and past states do influence routing performance. Moreover, common deep RL approaches use function approximators, such as neural networks, that do not model the spatial structure in network topologies. To address these shortcomings, we design a network environment with non-Markovian traffic and introduce a spatial-temporal RL (STRL) framework for packet routing. Our approach outperforms traditional baselines by more than 19% during training and 7% for inference despite a change in network topology. |
| title | Spatial-Temporal Reinforcement Learning for Network Routing with Non-Markovian Traffic |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.22174 |