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 multi-agent coordination. We evaluate our method in a high-fidelity telecommunications simulator, demonstrating that it outperforms state-of-the-art baselines. Our experiments show an improvement in satisfying temporally dependent intents compared to prior methods.