91 citations · 213 across the 33 of their papers we have counts for
5 papers · 1 filter
Finite-time Newton seeking control
Martin Guay, Mouhacine Benosman
This paper proposes a finite-time Newton seeking control design for systems described by unknown multivariable static maps. The Newton seeking system has an averaged system that im…
Robust Constrained-MDPs: Soft-Constrained Robust Policy Optimization under Model Uncertainty
Reazul Hasan Russel, Mouhacine Benosman, Jeroen Van Baar
In this paper, we focus on the problem of robustifying reinforcement learning (RL) algorithms with respect to model uncertainties. Indeed, in the framework of model-based RL, we pr…
On The Convergence of Euler Discretization of Finite-Time Convergent Gradient Flows
Siqi Zhang, Mouhacine Benosman, Orlando Romero
In this study, we investigate the performance of two novel first-order optimization algorithms, namely the rescaled-gradient flow (RGF) and the signed-gradient flow (SGF). These al…
Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization
Ankush Chakrabarty, Mouhacine Benosman
Data generated from dynamical systems with unknown dynamics enable the learning of state observers that are: robust to modeling error, computationally tractable to design, and capa…
Local Policy Optimization for Trajectory-Centric Reinforcement Learning
Patrik Kolaric, Devesh K. Jha, Arvind U. Raghunathan +4
The goal of this paper is to present a method for simultaneous trajectory and local stabilizing policy optimization to generate local policies for trajectory-centric model-based re…