4 citations · 9 across the 9 of their papers we have counts for
4 papers · 1 filter
Embedding Linear Equality Constraints in Probabilistic Neural Networks for Dynamic Modelling
Matthew Marsh, Benoit Chachuat, Antonio del Rio Chanona
Machine learning models are increasingly used to model chemical process systems, yet they often lack principled uncertainty quantification and mechanisms to enforce physical constr…
Expert-guided Bayesian Optimisation for Human-in-the-loop Experimental Design of Known Systems
Tom Savage, Ehecatl Antonio del Rio Chanona
Domain experts often possess valuable physical insights that are overlooked in fully automated decision-making processes such as Bayesian optimisation. In this article we apply hig…
An Analysis of Multi-Agent Reinforcement Learning for Decentralized Inventory Control Systems
Marwan Mousa, Damien van de Berg, Niki Kotecha +2
Most solutions to the inventory management problem assume a centralization of information that is incompatible with organisational constraints in real supply chain networks. The in…
Safe Chance Constrained Reinforcement Learning for Batch Process Control
Max Mowbray, Panagiotis Petsagkourakis, Ehecatl Antonio del Río Chanona +1
Reinforcement Learning (RL) controllers have generated excitement within the control community. The primary advantage of RL controllers relative to existing methods is their abilit…