A Reinforcement Learning Control Method for Process Industry Based on Implicit and Explicit Knowledge Extraction and Embedding
针对过程工业强化学习控制中状态动作空间高维、学习效率低和稳定性差的问题，提出一种通用知识提取方法，通过决策树提取隐式知识并嵌入控制器，同时设计显式知识导向的奖励函数来保证控制稳定性，在锌电积案例中降低了能耗并稳定了过程变量。
The process industry is a key manufacturing process that consumes a vast amount of energy consumption. On the premise of ensuring process stability, controlling process variables to operate the process close to the optimal working condition plays a critical role in reducing energy consumption. Reinforcement learning (RL), using trial and error to learn control strategies, has received much attention. However, the substantial fluctuations of process variables and the switching delay gap of the process industry result in a high-dimension state-action space, making it difficult to learn control strategies efficiently, and there is no guarantee of control stability. To get around these issues, first, a generic knowledge-extracted method for process industry RL control is proposed. It does not require laborious expert knowledge acquisition processes. Second, to improve learning efficiency, the implicit knowledge is extracted using decision trees from operation trajectory data and embedded into agent controllers. Third, an explicit knowledge-oriented reward constructing method is designed to guarantee control stability. A case of the zinc electrowinning process is provided to validate its superiority. The result shows that it can reduce power consumption while stabilizing process variables within the spec limits, without a laborious expert knowledge acquisition process.