mlsanityData to 7 Oct 2026

An Offline Deep Reinforcement Learning for Maintenance Decision-Making

arXiv 2109.15050 ↗First posted 28 Sep 2021cs.LG cs.AIcited by 2

On the map: Offline RL and imitation learning from data → Offline RL algorithms

Abstract (arXiv)

Several machine learning and deep learning frameworks have been proposed to solve remaining useful life estimation and failure prediction problems in recent years. Having access to the remaining useful life estimation or likelihood of failure in near future helps operators to assess the operating conditions and, therefore, provides better opportunities for sound repair and maintenance decisions. However, many operators believe remaining useful life estimation and failure prediction solutions are incomplete answers to the maintenance challenge. They argue that knowing the likelihood of failure in the future is not enough to make maintenance decisions that minimize costs and keep the operators safe. In this paper, we present a maintenance framework based on offline supervised deep reinforcement learning that instead of providing information such as likelihood of failure, suggests actions such as "continuation of the operation" or "the visitation of the repair shop" to the operators in order to maximize the overall profit. Using offline reinforcement learning makes it possible to learn the optimum maintenance policy from historical data without relying on expensive simulators. We demonstrate the application of our solution in a case study using the NASA C-MAPSS dataset.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Offline RL algorithms, one of 13 directions in Offline RL and imitation learning from data. Builds on 7 of its 10 references that are in the map. First posted on arXiv 28 Sep 2021.

Builds on

7 of 10 references.

References · 7

  1. 2017Attention Is All You Needcited by 59,932
  2. 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,528
  3. 2021Offline Reinforcement Learning as One Big Sequence Modeling Problemcited by 636
  4. 2019Reinforcement Learning Upside Down: Don't Predict Rewards -- Just Map Them to Actionscited by 124
  5. 2021Simplifying Deep Reinforcement Learning via Self-Supervisioncited by 9
  6. 2019Remaining Useful Life Estimation Using Functional Data Analysiscited by 7
  7. 2020Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatchingcited by 4

Built on by

Cited by year

Citing papers per year: 2022: 2.

2 papers.

  1. 2022On Transforming Reinforcement Learning by Transformer: The Development Trajectorycited by 35
  2. 2022A Maintenance Planning Framework using Online and Offline Deep Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2021Reinforcement Learning based Condition-oriented Maintenance Scheduling for Flow Line Systems
  2. 2026Uncertainty and Business-Aware Remaining Useful Life Estimation for Semiconductor Manufacturing
  3. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
  4. 2026Label-free Industrial Fault Detection via Adversarial Inverse Reinforcement Learning: A System for Run-to-Failure Prognostics
  5. 2026Learning Rewards, Not Labels: Adversarial Inverse Reinforcement Learning for Machinery Fault Detection
  6. 2026Failure-Aware RL: Reliable Offline-to-Online Reinforcement Learning with Self-Recovery for Real-World Manipulation
  7. 2022A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems
  8. 2022Data Valuation for Offline Reinforcement Learning