Abstract
Maintenance policy optimization is crucial for ensuring the efficient functioning of structures and systems and mitigating the risk of deterioration. Reinforcement learning methods, especially when combined with deep neural networks, have seen significant progress in supporting maintenance decisions. However, deep reinforcement learning (DRL) typically necessitates an extensive number of interactions with the system to acquire the optimal policy, resulting in data inefficiency issues that limit the application of DRL in practical engineering fleet problems. Deriving optimal policies with DRL repeatedly for every individual in the engineering fleet can be computationally expensive or even prohibitive. To address the data inefficiency issues, this study proposes a novel maintenance optimization approach that can transfer knowledge from previously learned maintenance cases to the new cases to accelerate the DRL process. Meta-reinforcement learning (MetaRL) method is proposed to realize the concept of knowledge transfer within a fleet by learning a meta-learned policy. In particular, the meta-learned policy can be quickly adapted to each individual case of the engineering fleet, thereby reducing the required computational burden for maintenance policy optimization. Two examples are used to demonstrate the effectiveness of knowledge transfer.