论文标题
冶金腌制线上的自适应速度调节的基于学习的基于学习的多代理合作方法
The reinforcement learning-based multi-agent cooperative approach for the adaptive speed regulation on a metallurgical pickling line
论文作者
论文摘要
在冶金腌制线的示例中，我们提出了一种整体数据驱动的方法来提高生产率的问题。提出的方法将数学建模与基本算法和实施的合作多代理增强学习（MARL）系统相结合，例如通过多个标准增强性能，同时还满足安全性和可靠性要求并考虑到某些技术流程的意外波动。我们证明了如何将深度Q学习应用于重工业中的现实生活任务，从而大大改善了先前现有的自动化系统。输入数据稀缺的问题通过LSTM和CGAN的两步组合解决了，这有助于接受数据的表格及其顺序属性。通过复杂的概率运动环境，离线RL培训是在这种情况下的必要性。
We present a holistic data-driven approach to the problem of productivity increase on the example of a metallurgical pickling line. The proposed approach combines mathematical modeling as a base algorithm and a cooperative Multi-Agent Reinforcement Learning (MARL) system implemented such as to enhance the performance by multiple criteria while also meeting safety and reliability requirements and taking into account the unexpected volatility of certain technological processes. We demonstrate how Deep Q-Learning can be applied to a real-life task in a heavy industry, resulting in significant improvement of previously existing automation systems.The problem of input data scarcity is solved by a two-step combination of LSTM and CGAN, which helps to embrace both the tabular representation of the data and its sequential properties. Offline RL training, a necessity in this setting, has become possible through the sophisticated probabilistic kinematic environment.
