4 papers
Enhanced Reinforcement Learning-based Process Synthesis via Quantum Computing
Austin Braniff, Fengqi You, Yuhe Tian
In this work, we present quantum reinforcement learning (RL) as a solution strategy for process synthesis problems. Building on our prior work, we develop a generalized framework t…
Reinforcement Learning-based Control via Y-wise Affine Neural Networks: Comparative Case Studies for Chemical Processes
Austin Braniff, Yuhe Tian
In this work we present an efficient and practically implementable approach for the application of reinforcement learning (RL)-based control in chemical process systems. This is an…
Reinforcement Learning-based Control via Y-wise Affine Neural Networks (YANNs)
Austin Braniff, Yuhe Tian
This work presents a novel reinforcement learning (RL) algorithm based on Y-wise Affine Neural Networks (YANNs). YANNs provide an interpretable neural network which can exactly rep…
YANNs: Y-wise Affine Neural Networks for Exact and Efficient Representations of Piecewise Linear Functions
Austin Braniff, Yuhe Tian
This work formally introduces Y-wise Affine Neural Networks (YANNs), a fully-explainable network architecture that continuously and efficiently represent piecewise affine functions…