Effective transportation network management systems should consider safety and sustainability objectives. Existing research on large-scale transportation network management often employs the assumption that bridges can be considered individually under these objectives. However, this simplification misses accurate system-level representations, induced by multiple components, network topology, and global maintenance actions. To address these limitations, this paper presents a deep reinforcement learning (DRL) framework that draws inspiration from biological learning behaviors to determine optimal life-cycle management policies. It incorporates synergetic branches and hierarchical rewards, factorizing the action space and, thereby, diminishing system complexity from exponential to linear with respect to the number of bridges. Extensive experiments based on a realistic case study demonstrate that the proposed method outperforms expert maintenance strategies and state-of-the-art decision-making methods. Overall, the proposed DRL framework can assist engineers by offering adaptive solutions to maintenance planning. It also provides solutions that address large action spaces within complex systems.