可重入生产系统的递阶增强型学习调度
HIERARCHICAL REINFORCEMENT LEARNING SCHEDULE FOR RE-ENTRANT MANUFACTURING SYSTEM
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摘要: 对平均报酬型马氏决策过程，本文研究了一种递阶增强型学习算法；并将算法应用于一个两台机器组成的闭环可重入生产系统，计算机仿真结果表明，调度结果优于熟知的两种启发式调度策略．Abstract: In this paper,a hierarchical reinforcement learning algorithm is investigated for Markov Decision Process with average reward.And it is applied to a close re entrant manufacturing system composed of two machines.Computer simulation demonstrates that the algorithm outperforms two well known heuristic scheduling policies.
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