In Chapter 7, we frame the sequential decision-making problem using Deep Reinforcement Learning (DRL) for component-level maintenance of sewer mains. This framework considers damage severity levels, testing di!erent deterioration model assumptions and evaluating their impact on maintenance policy. Our results show that agent-based policies outperformed heuristics by learning optimal sequences of maintenance actions. Part III provides evidence that DRL-based techniques o!er a flexible framework with the potential to improve heuristics and support maintenance decision-making for sewer mains. However, training these models to achieve the desired behaviour remains a challenging task.
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