Integrated Access and Backhaul (IAB) networks

offer a versatile and scalable solution for expanding broadband

coverage in urban environments. However, optimizing the deploy-

ment of IAB nodes to ensure reliable coverage while minimizing

costs poses significant challenges, particularly given the location

constraints and the highly dynamic nature of urban settings. This

work introduces a novel Deep Reinforcement Learning (DRL)

approach for IAB network planning, considering urban con-