125 citations · 182 across the 4 of their papers we have counts for
4 papers
Meta-Reinforcement Learning for Adaptive Control of Second Order Systems
Daniel G. McClement, Nathan P. Lawrence, Michael G. Forbes +3
Meta-learning is a branch of machine learning which aims to synthesize data from a distribution of related tasks to efficiently solve new ones. In process control, many systems hav…
Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approach
Daniel G. McClement, Nathan P. Lawrence, Johan U. Backstrom +3
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, ma…
Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning
Nathan P. Lawrence, Michael G. Forbes, Philip D. Loewen +3
Deep reinforcement learning (RL) is an optimization-driven framework for producing control strategies for general dynamical systems without explicit reliance on process models. Goo…
A Meta-Reinforcement Learning Approach to Process Control
Daniel G. McClement, Nathan P. Lawrence, Philip D. Loewen +3
Meta-learning is a branch of machine learning which aims to quickly adapt models, such as neural networks, to perform new tasks by learning an underlying structure across related t…