most citedComputationally efficient Gauss-Newton reinforcement learning for model predictive control

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eess.SY2025★ 1 cited

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…

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…