# DQLAP: Deep Q-Learning Recommender Algorithm with Update Policy for a Real Steam Turbine System

arXiv: https://arxiv.org/abs/2210.06399 · HTML: https://mlsanity.com/paper/2210.06399/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 6 of 6)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks](https://mlsanity.com/paper/1803.11115/) (2018)
- [Deep Reinforcement Learning for Imbalanced Classification](https://mlsanity.com/paper/1901.01379/) (2019)

## Built on by (most cited first, 1 of 1)

- [Imbalanced Classification In Faulty Turbine Data: New Proximal Policy Optimization](https://mlsanity.com/paper/2301.04049/) (2023)

## Abstract (arXiv)

In modern industrial systems, diagnosing faults in time and using the best methods becomes more and more crucial. It is possible to fail a system or to waste resources if faults are not detected or are detected late. Machine learning and deep learning have proposed various methods for data-based fault diagnosis, and we are looking for the most reliable and practical ones. This paper aims to develop a framework based on deep learning and reinforcement learning for fault detection. We can increase accuracy, overcome data imbalance, and better predict future defects by updating the reinforcement learning policy when new data is received. By implementing this method, we will see an increase of 3% in all evaluation metrics, an improvement in prediction speed, and 3% - 4% in all evaluation metrics compared to typical backpropagation multi-layer neural network prediction with similar parameters.