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中文摘要：
针对当前基于Q-学习的Agent生产调度优化研究甚少的现状，利用Q-学习对动态单机调度问题在3种不同系统目标下的调度规则动态选择问题进行了研究．在建立Q-学习与动态单机调度问题映射机制的基础上，通过MATLAB实验仿真，对算法性能进行了评价．仿真结果表明，对于不同的系统调度目标，Q-学习能提高Agent的适应能力，达到单一调度规则无法达到的性能，适合基于Agent的动态生产调度环境．
英文摘要：
Q-learning was applied to a dynamic single-machine scheduling problem. Corresponding to the environment status change and three predefined system performance measurement, the machine agent that is embedded with Q-learning can select an appropriate dispatching rule dynamically. Based on the model between Q-learning and the dynamic single-machine scheduling problem, the performance of Q-learning was evaluated through simulations in MATLABa environment. The simulation results demonstrate that Q-learning can perform well for different system objectives, which is impossible for single dispatching rule. Therefore, Q-learning is promising for application to the agent-based dynamic production scheduling.
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