Online Single Machine Scheduling Based on Simulation and Reinforcement Learning

In this paper, an online single machine environment is used to investigate the application potential of reinforcement learning for job shop scheduling problems, to quantify states and actions, to define reward functions, and to examine their influences on the performance of the algorithm through plenty of experiments. The findings from this research are expected to be the basis for further investigations into applying reinforcement learning to more complex job shop environments in the future.