mlsanityData to 14 Oct 2026

Deep Multi-Objective Reinforcement Learning for Utility-Based Infrastructural Maintenance Optimization

arXiv 2406.06184 ↗First posted 10 Jun 2024cs.AI cs.LGcited by 1

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 16 of its 49 references that are in the map. First posted on arXiv 10 Jun 2024.

Builds on

16 of 49 references.

References · 16

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4 more
  1. 2020Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraintscited by 18
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026COOL-MC: Verifying and Explaining RL Policies for Multi-bridge Network Maintenancecited by 0

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  4. 2022A Maintenance Planning Framework using Online and Offline Deep Reinforcement Learning
  5. 2024In Search for Architectures and Loss Functions in Multi-Objective Reinforcement Learning
  6. 2025Hierarchical Deep Reinforcement Learning Framework for Multi-Year Asset Management Under Budget Constraints
  7. 2025Multi-Year Maintenance Planning for Large-Scale Infrastructure Systems: A Novel Network Deep Q-Learning Approach
  8. 2024Multi-agent deep reinforcement learning with centralized training and decentralized execution for transportation infrastructure management