01559nas a2200109   4500000000100000008004100001100001400042700001600056700002600072245011400098520123700212       2025                            d1 aQiong Liu1 aLimao Zhang1 aMirosław Skibniewski00aNetwork extension planning towards resilient urban critical infrastructures using deep reinforcement learning3 a<p>As cities continue to grow, expanding metro networks becomes essential for optimizing urban transportation efficiency. Therefore, scientific strategic planning of metro networks is indispensable. This study proposes a deep reinforcement learning (DRL) approach to discover the optimal planning strategy for metro network extension. The model integrates multi-source data into the reward function while customizing the state and action spaces to reflect the unique characteristics of metro networks. A policy network is developed using an Encoder-Decoder framework, with the parameters being updated by an Actor-Critic framework based on policy gradient. A comprehensive performance index is proposed to evaluate the vulnerability and service capacity of planned networks. The proposed method is validated through a case study on the Hangzhou metro system. The results demonstrate that the proposed DRL can result in optimal planned networks that outperform the actually implemented network with a maximum Performance Improvement Percentage of 19.87 \%. The DRL-based optimization framework proposed for metro network extension planning is anticipated to enhance adaptability towards urban development and increase resilience.</p>