3 papers
eess.SY2026
Computationally efficient Gauss-Newton reinforcement learning for model predictive control
Dean Brandner, Sebastien Gros, Sergio Lucia
Model predictive control (MPC) is widely used in process control due to its interpretability and ability to handle constraints. As a parametric policy in reinforcement learning (RL…
cs.LG2025
Optimizing Operation Recipes with Reinforcement Learning for Safe and Interpretable Control of Chemical Processes
Dean Brandner, Sergio Lucia
Optimal operation of chemical processes is vital for energy, resource, and cost savings in chemical engineering. The problem of optimal operation can be tackled with reinforcement…
cs.LG2025
Quasi-Newton Compatible Actor-Critic for Deterministic Policies
Arash Bahari Kordabad, Dean Brandner, Sebastien Gros +2
In this paper, we propose a second-order deterministic actor-critic framework in reinforcement learning that extends the classical deterministic policy gradient method to exploit c…