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Imitation Learning Based on Deep Reinforcement Learning for Solving Scheduling Problems.

, , , and . HICSS, page 1649-1658. ScholarSpace, (2024)

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A Recent Publications Survey on Reinforcement Learning for Selecting Parameters of Meta-Heuristic and Machine Learning Algorithms., , and . CLOSER, page 236-243. SCITEPRESS, (2023)Data Driven Meta-Heuristic-Assisted Approach for Placement of Standard IT Enterprise Systems in Hybrid-Cloud., , , and . CLOSER, page 139-146. SCITEPRESS, (2023)Comparative analysis of machine learning models for anomaly detection in manufacturing., , , and . ISM, volume 200 of Procedia Computer Science, page 1288-1297. Elsevier, (2021)Deep Reinforcement Learning Techniques For Solving Hybrid Flow Shop Scheduling Problems: Proximal Policy Optimization (PPO) and Asynchronous Advantage Actor-Critic (A3C)., , and . HICSS, page 1-10. ScholarSpace, (2022)A Hybrid Job Scheduling Approach on Cloud Computing Environments on the Usage of Heuristics and Metaheuristics Methods., , , and . HICSS, page 1580-1589. ScholarSpace, (2023)Imitation Learning Based on Deep Reinforcement Learning for Solving Scheduling Problems., , , and . HICSS, page 1649-1658. ScholarSpace, (2024)Utility of Univariate Forecasting for Workload Metrics Predictions in Enterprise Applications., , , and . KMIS, page 231-240. SCITEPRESS, (2023)Addressing IT Capacity Management Concerns Using Machine Learning Techniques., , , , and . SN Comput. Sci., 3 (1): 26 (2022)Hybrid Approach for Solving Multi-Objective Hybrid Flow Shop Scheduling Problems with Family Setup Times., , , and . ISM, volume 200 of Procedia Computer Science, page 1685-1694. Elsevier, (2021)Investigating different optimization criteria for a hybrid job scheduling approach based on heuristics and metaheuristics., , , and . ACIS, page 48. (2022)