Operation and maintenance of the built environment have a major effect on socioeconomic stability and sustainability. A significant part of our built world approaches or has well exceeded its designated structural life. As engineers, we need to find efficient ways to extend this life while maintaining acceptable levels of safety and performance. In this direction, smart and adaptive life-cycle inspection and maintenance planning are of paramount importance to reduce costs, increase structural reliability, and minimize resource-intensive interventions. Deep reinforcement learning provides a novel approach to strategize these decisions for systems subject to uncertainties and deterioration. The goal of this project is to determine optimal life-cycle inspection and maintenance policies for road networks. Road maintenance and inspection planning is a complex task, involving a multitude of different objectives, such as the minimization of lifecycle cost and CO2 emissions (both from the maintenance works and vehicle emissions). However, current literature has underestimated the importance of optimizing for multiple objectives, and doesn’t consider the environmental impact during planning. In the current project, we aim to optimize the maintenance and inspection schedule with respect to minimizing the maintenance costs, carbon emissions and vehicle owners costs at the same time. To achieve that, a multi-objective road network environment will be modelled and multiple multi-objective Deep Reinforcement learning approaches will be applied and compared to traditional life-cycle management policies.