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Temporal Intent-Aware Multi-agent Learning for Network Optimization
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DOI：10.1007/978-3-032-02018-5_3.png)
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Abstract
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Cellular networks have grown in size and complexity in recent years. To meet increasing traffic demands, new approaches are needed to replace legacy rule-based controllers and network management systems. Among these, learning-based methods are appealing because they can discover control policies without relying on expert knowledge. Intent-based networking, which describes desired network behavior rather than specific configurations, introduces a new level of abstraction. However, satisfying network intents under temporal constraints remains an open challenge. In this paper, we present a reinforcement learning approach that leverages Signal Temporal Logic (STL) to quantitatively translate network intents into a reward signal. We combine this with a transformer-based neural network architecture to handle temporal dependencies and multiagent coordination. We evaluate our method in a high-fidelity telecommunications simulator, demonstrating that it outperforms state-of-theart baselines. Our experiments show an improvement in satisfying temporally dependent intents compared to prior methods.
Keywords:
Reinforcement learning
Temporal logic
Network optimization
Intent-driven control
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