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Document type:
Preprint
Author(s):
Daniel Hettegger; Carmen Buliga; Florian Walter; Elizabeth Bismut; Daniel Straub; Alois Knoll
Title:
Investigation of Inspection and Maintenance Optimization with Deep Reinforcement Learning in Absence of Belief States
Abstract:
Maintenance of deteriorating infrastructure is a major cost factor for owners and society. Therefore, efficient inspection and maintenance (I&M;) strategies are of paramount importance. Deep reinforcement learning (DRL) has been proposed for maintenance optimization of deteriorating systems. For good performance, DRL relies on information rich state representations, but information about the state may only be available through costly inspections. One option to alleviate this is by use of belief s...     »
Keywords:
Deep Reinforcement Learning; Infrastructure Maintenance Optimization
Journal title:
ICASP14 - 14th International Conference on Application of Statistics and Probability in Civil Engineering
Year:
2023
Reviewed:
ja
Language:
en
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