A STUDY OF REINFORCEMENT LEARNING APPLIED TO DYNAMIC SINGLE-MACHINE JOB DISPATCHING

摘要

Reinforcement learning (RL) has received some attention in recent years from agent-based researchers because it deals with the problem of how an autonomous agent can learn to select proper actions for achieving its goals through interacting with its environment. Each time after an agent performs an action, the environment's response, as indicated by its new state, is used by the agent to reward or penalize its action. The agent's goal is to maximize the total amount of reward it receives over the long run. Although there have been several successful examples demonstrating the usefulness of RL, its application to manufacturing systems has not been fully explored. In this paper, Q-learning, a popular RL algorithm, is applied to the dispatching rule selection problem on a single machine. The goal was to determine if a single machine agent is able to learn the optimal rules for three examples in which the optimal dispatching rules have been previously defined. This study provided encouraging results that show the potential of reinforcement learning for application to agent-based production scheduling.