Path-aware Internet architectures such as SCION expose multiple end-to-end paths to endhosts. However, optimal path selection in dynamic network conditions remains challenging. This paper presents a Deep-Q-Network approach for intelligent path selection in SCION networks. We formulate SCION path selection as a reinforcement learning problem and train a lightweight Deep-Q-Network agent that observes latency, loss, and bandwidth metrics and outputs the optimal path. We evaluate our approach in an simulated SCION environment with realistic time-varying traffic conditions. The DQN agent consistently matches the performance of oracle-based selection methods with full network visibility, while reducing probing overhead by 95%. These results demonstrate the potential of reinforcement learning to effectively leverage the path diversity and control offered by next-generation Internet architectures.