5 citations · 9 across the 4 of their papers we have counts for
5 papers
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…
Distributional Reinforcement Learning for Scheduling of Chemical Production Processes
Max Mowbray, Dongda Zhang, Ehecatl Antonio Del Rio Chanona
Reinforcement Learning (RL) has recently received significant attention from the process systems engineering and control communities. Recent works have investigated the application…
Integrating process design and control using reinforcement learning
Steven Sachio, Max Mowbray, Maria Papathanasiou +2
To create efficient-high performing processes, one must find an optimal design with its corresponding controller that ensures optimal operation in the presence of uncertainty. When…
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…
Constrained Model-Free Reinforcement Learning for Process Optimization
Elton Pan, Panagiotis Petsagkourakis, Max Mowbray +2
Reinforcement learning (RL) is a control approach that can handle nonlinear stochastic optimal control problems. However, despite the promise exhibited, RL has yet to see marked tr…