Maritime ports are critical nodes in global logistics network, but are vulnerable to disruptions, especially from extreme weather events. Efficient recovery by optimized repair sequences can minimize vessel waiting times and operational losses. This study proposes a Deep
Q-Network framework integrated with Agent-Based modelling to optimize repair strategies
for disrupted waterway networks in ports. A case study of the Port of Rotterdam demonstrates
the model structure, implementation and comparison with a greedy heuristic benchmark. The
DQN was trained on simulated disruption scenarios and evaluated on unseen test cases, showing superior performance in high priority disruptions. In low priority scenarios with minimal
network impact, the greedy approach performed comparably or better. Results highlight the
potential of reinforcement learning to adapt repair strategies dynamically based on real-time
conditions, offering a decision support tool for port authorities. The findings suggest that, with
further integration of operational constraints and real world data, such methods could enhance
port resilience and reduce recovery times following major disruptions.