Meta-reinforcement learning for the tuning of PI controllers: An offline approach. (October 2022) - McClement, Daniel G.; Lawrence, Nathan P.; Backström, Johan U.; Loewen, Philip D.; Forbes, Michael G.; Gopaluni, R. Bhushan
Abstract: Meta-learning is a branch of machine learning which trains neural network models to synthesize a wide variety of data in order to rapidly solve new problems. In process control, many systems have similar and well-understood dynamics, which suggests it is feasible to create a generalizable controller through meta-learning. In this work, we formulate a meta reinforcement learning (meta-RL) control strategy that can be used to tune proportional–integral controllers. Our meta-RL agent has a recurrent structure that accumulates "context" to learn a system's dynamics through a hidden state variable in closed-loop. This architecture enables the agent to automatically adapt to changes in the process dynamics. In tests reported here, the meta-RL agent was trained entirely offline on first order plus time delay systems, and produced excellent results on novel systems drawn from the same distribution of process dynamics used for training. A key design element is the ability to leverage model-based information offline during training in simulated environments while maintaining a model-free policy structure for interacting with novel processes where there is uncertainty regarding the true process dynamics. Meta-learning is a promising approach for constructing sample-efficient intelligent controllers. Highlights: An expert agent recognizes and controls any process in a large class. Offline training leads to sample-efficient online operation and adaptation. The online agent adapts automatically as plant characteristics drift. The agent adjusts PI parameters instead of directly actuating the plant. meta-RL design generalizes to various plant and controller structures.