摘要
为优化PLC控制系统的性能,提出了一种改进的Q-Learning强化学习算法。该算法在每次迭代优化过程中,利用模型预测未来的状态集,并在这些状态中选择了预期收益最大的决策。通过模拟实验,可发现该算法在控制相关的性能指标上具有明显优势。
In order to optimize the performance of PLC control systems,this paper proposes an improved Q-Learning reinforcement learning algorithm.This algorithm utilizes the model to predict the future state set during each iterative optimization process,and selects the decision that maximizes the expected return among these states.Through simulation experiments,it can be seen that this algorithm has significant advantages in controlling related performance indicators.
作者
张瑞宽
ZHANG Ruikuan(Dagang Oilfield Branch of China National Petroleum Corporation Limited,Tianjin 300280,China)
出处
《自动化应用》
2024年第6期70-71,74,共3页
Automation Application
关键词
强化学习
迭代优化
Q-Learning强化学习算法
reinforcement learning
iterative optimization
Q-Learning reinforcement learning algorithm
