In this paper, we introduce Multi-Objective Deep Centralized Multi-Agent Actor-Critic (MO- DCMAC), a multi-objective reinforcement learning (MORL) method for infrastructural maintenance optimization, an area traditionally dominated by single-objective reinforcement learning (RL) approaches. Previous single-objective RL methods combine multiple objectives, such as probability of collapse and cost, into a singular reward signal through reward-shaping. In contrast, MO-DCMAC can optimize a policy for multiple objectives directly, even when the utility function is non-linear. We evaluated MO-DCMAC using two utility functions, which use probability of collapse and cost as input. The first utility function is the Threshold utility, in which MO-DCMAC should minimize cost so that the probability of collapse is never above the threshold. The second is based on the Failure Mode, Effects, and Criticality Analysis (FMECA) methodology used by asset managers to asses maintenance plans. We evaluated MO-DCMAC, with both utility functions, in multiple maintenance environments, including ones based on a case study of the historical quay walls of Amsterdam. The performance of MO-DCMAC was compared against multiple rule-based policies based on heuristics currently used for constructing maintenance plans. Our results demonstrate that MO-DCMAC outperforms traditional rule-based policies across various environments and utility functions.

翻译：本文介绍了多目标深度集中式多智能体行动者-评论家方法（MO-DCMAC），这是一种用于基础设施维护优化的多目标强化学习方法，该领域传统上由单目标强化学习方法主导。先前的单目标强化学习方法通过奖励塑形将多个目标（如倒塌概率和成本）合并为单一奖励信号。相比之下，MO-DCMAC能够直接针对多个目标优化策略，即使在效用函数是非线性的情况下。我们使用两种以倒塌概率和成本为输入的效用函数评估了MO-DCMAC。第一种效用函数是阈值效用，MO-DCMAC应在此条件下最小化成本，使得倒塌概率始终不超过阈值。第二种基于失效模式、影响与危害性分析方法，资产管理者常用此方法来评估维护计划。我们在多种维护环境中评估了MO-DCMAC在两种效用函数下的表现，包括基于阿姆斯特丹历史码头墙案例研究的环境。MO-DCMAC的性能与多种基于当前用于制定维护计划的启发式规则的策略进行了比较。我们的结果表明，在各种环境和效用函数下，MO-DCMAC均优于传统的基于规则的策略。