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Meta-Reinforcement Learning for Adaptive Control of Second Order Systems
In this work, we briefly reintroduce our methodology and demonstrate how it can be extended to proportional-integral-derivative controllers and second order systems.
Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approach
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.
Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning
In addition to its simplicity, this approach has several appealing features: No additional hardware needs to be added to the control system, since a PID controller can easily be implemented through a standard programmable logic controller; the control law can easily be initialized in a "safe'' region of the parameter space; and the final product -- a well-tuned PID controller -- has a form that practitioners can reason about and deploy with confidence.
A Meta-Reinforcement Learning Approach to Process Control
Meta-learning is appealing for process control applications because the perturbations to a process required to train an AI controller can be costly and unsafe.
