# An Offline Deep Reinforcement Learning for Maintenance Decision-Making

arXiv: https://arxiv.org/abs/2109.15050 · HTML: https://mlsanity.com/paper/2109.15050/

## Key facts

- Cited by 2 papers in the map (data through 14 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.

## Placed in

- Offline RL and imitation learning from data / [Offline RL algorithms](https://mlsanity.com/offlinerl/offline-rl-algorithms/)

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://mlsanity.com/paper/2106.02039/) (2021)
- [Reinforcement Learning Upside Down: Don't Predict Rewards -- Just Map Them to Actions](https://mlsanity.com/paper/1912.02875/) (2019)
- [Simplifying Deep Reinforcement Learning via Self-Supervision](https://mlsanity.com/paper/2106.05526/) (2021)
- [Remaining Useful Life Estimation Using Functional Data Analysis](https://mlsanity.com/paper/1904.06442/) (2019)
- [Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatching](https://mlsanity.com/paper/2011.05570/) (2020)

## Built on by (most cited first, 2 of 2)

- [On Transforming Reinforcement Learning by Transformer: The Development Trajectory](https://mlsanity.com/paper/2212.14164/) (2022)
- [A Maintenance Planning Framework using Online and Offline Deep Reinforcement Learning](https://mlsanity.com/paper/2208.00808/) (2022)

## 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.