Meta Reinforcement Learning for Adaptive Control: An Offline Approach

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 takes advantage of known, offline information for training, such as the system gain or time constant, yet efficiently controls novel systems in a completely model-free fashion. Our meta-RL agent has a recurrent structure that accumulates "context" for its current dynamics through a hidden state variable. This end-to-end architecture enables the agent to automatically adapt to changes in the process dynamics. Moreover, the same agent can be deployed on systems with previously unseen nonlinearities and timescales. In tests reported here, the meta-RL agent was trained entirely offline, yet produced excellent results in novel settings. A key design element is the ability to leverage model-based information offline during training, while maintaining a model-free policy structure for interacting with novel environments. To illustrate the approach, we take the actions proposed by the meta-RL agent to be changes to gains of a proportional-integral controller, resulting in a generalized, adaptive, closed-loop tuning strategy. Meta-learning is a promising approach for constructing sample-efficient intelligent controllers.