Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions

4 October 2023

Papers citing "Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions"

7 / 7 papers shown

Title
FORLAPS: An Innovative Data-Driven Reinforcement Learning Approach for Prescriptive Process Monitoring Mostafa Abbasi Maziyar Khadivi Maryam Ahang Patricia Lasserre Yves Lucet H. Najjaran OffRL 32 1 0 17 Jan 2025
Generative Edge Detection with Stable Diffusion Caixia Zhou Yaping Huang Mochu Xiang Jiahui Ren Haibin Ling Jing Zhang 43 0 0 04 Oct 2024
Reinforcement and Deep Reinforcement Learning-based Solutions for Machine Maintenance Planning, Scheduling Policies, and Optimization Oluwaseyi Ogunfowora H. Najjaran OffRL 14 42 0 07 Jul 2023
Synthesizing Rolling Bearing Fault Samples in New Conditions: A framework based on a modified CGAN Maryam Ahang Masoud Jalayer Ardeshir Shojaeinasab Oluwaseyi Ogunfowora Todd Charter H. Najjaran AI4CE 46 18 0 24 Jun 2022
Real-Time Neural Network Scheduling of Emergency Medical Mask Production during COVID-19 Chen-Xin Wu Min-Hui Liao M. Karatas Shengyong Chen Yujun Zheng 15 34 0 28 Jul 2020
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning Jakob N. Foerster Nantas Nardelli Gregory Farquhar Triantafyllos Afouras Philip H. S. Torr Pushmeet Kohli Shimon Whiteson OffRL 109 594 0 28 Feb 2017
Neural Combinatorial Optimization with Reinforcement Learning Irwan Bello Hieu H. Pham Quoc V. Le Mohammad Norouzi Samy Bengio 69 1,447 0 29 Nov 2016