The reinforcement learning-based multi-agent cooperative approach for the adaptive speed regulation on a metallurgical pickling line
This work addresses productivity optimization in heavy industry, specifically for metallurgical pickling lines, but it is incremental as it applies known RL methods to a new domain.
The authors tackled the problem of increasing productivity on a metallurgical pickling line by developing a multi-agent reinforcement learning approach that combines mathematical modeling with MARL, resulting in significant improvements over existing automation systems.
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.