mlsanityData to 14 Oct 2026

Spatial-Temporal Reinforcement Learning for Network Routing with Non-Markovian Traffic

arXiv 2507.22174 ↗First posted 27 Jul 2025cs.LG cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 24 references that are in the map. First posted on arXiv 27 Jul 2025.

Builds on

6 of 24 references.

References · 6

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  3. 2015Continuous control with deep reinforcement learningcited by 3,407
  4. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  5. 2022DRL-M4MR: An Intelligent Multicast Routing Approach Based on DQN Deep Reinforcement Learning in SDNcited by 6
  6. 2020STDPG: A Spatio-Temporal Deterministic Policy Gradient Agent for Dynamic Routing in SDNcited by 1

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  4. 2024Intelligent Routing Algorithm over SDN: Reusable Reinforcement Learning Approach
  5. 2025Simulation-Driven Reinforcement Learning in Queuing Network Routing Optimization
  6. 2025A Flexible Multi-Agent Deep Reinforcement Learning Framework for Dynamic Routing and Scheduling of Latency-Critical Services
  7. 2025Reinforcement Learning for Dynamic Resource Allocation in Optical Networks: Hype or Hope?
  8. 2024Closed-form congestion control via deep symbolic regression