A Reinforcement Learning Implementation for a Scheduling Problem

This chapter delves into the application of reinforcement learning to tackle scheduling problems, specifically focusing on minimizing total tardiness in single-machine job processing. The study introduces a Q-Learning model, detailing the methodology for defining states, actions, and rewards. It also explores the integration of machine learning techniques in scheduling problems, highlighting the growing interest in reinforcement learning for complex tasks. The experimental results compare the Q-Learning algorithm with the tabu search algorithm, demonstrating the former's superior performance and efficiency. The study concludes with a discussion on future research opportunities, including the potential of more sophisticated reinforcement learning methods and advanced deep learning architectures.