Multiagent Reinforcement Learning for Project-Level Intervention Planning under Multiple Uncertainties

Reinforcement learning (RL) has recently been adopted by infrastructure asset management (IAM) researchers for adding flexibility regarding uncertainties in preventive actions decision-making. However, this relatively recent line of research has not incorporated other sources of uncertainties, such as hazards apart from deterioration patterns, nor has such research considered managerial aspects of IAM, such as stakeholders' utilities. This paper aims to provide a holistic framework that draws upon recent developments in IAM systems and microworlds, employs RL model training, and considers deterioration, hazards, and cost fluctuations as the main sources of uncertainties while also adopting managerial aspects into decision-making. Consistent with the existing practice of IAM, this framework brings flexibility in the face of uncertainties to the IAM decision-making process. Multi-agent RL models based on deep Q networks and actor-critic models are constructed and trained for taking intervention actions regarding elements of a real bridge in Indiana through its life cycle. Both models could lead to higher expected utilities and lower costs compared to the optimal maintenance, rehabilitation, and reconstruction (MRR) plans obtained by Monte Carlo simulation and heuristic optimization algorithms. The proposed framework can assist decision-making bodies and managers in the IAM domain with making updateable optimal and more realistic decisions based on the updated state of various complex uncertainties in a negligible amount of time.

Automated summary

This paper introduces the use of reinforcement learning in infrastructure asset management and proposes a holistic framework that considers deterioration, hazards, and cost fluctuations as sources of uncertainties while incorporating managerial aspects. By constructing and training multi-agent reinforcement learning models, the framework provides flexibility in decision-making in the face of uncertainties.