Decision-making for road infrastructures in a network based on a policy gradient method

Developing proper maintenance and rehabilitation investment plans is vital for prolonging the service life of road infrastructures while preserving required service level under capital constraints. This paper proposes a reinforcement learning approach for determining an optimal policy of selecting maintenance, repair, and rehabilitation alternatives for a network of road infrastructure facilities. The proposed approach is based on a policy gradient method and overcomes the computational complexity of optimization problems due to a large number of possible combinations of the network conditions and maintenance, repair, and rehabilitation alternatives. The developed optimal management policy takes into consideration interdependencies among infrastructure facilities in a road network. Numerical studies on concrete bridge decks in road networks are performed to demonstrate the advantage, feasibility, and capability of the proposed approach.