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
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…
eess.SY2025
Data-Driven Distributionally Robust Control for Interacting Agents under Logical Constraints
Arash Bahari Kordabad, Eleftherios E. Vlahakis, Lars Lindemann +3
In this paper, we propose a distributionally robust control synthesis for an agent with stochastic dynamics that interacts with other agents under uncertainties and constraints exp…