activity
20212026
most citedA Crash Course on Reinforcement Learning

3 citations · 3 across the 3 of their papers we have counts for

collaborators

5 papers

eess.SY2026

Direct Data Driven Natural Gradient Descent for Control

Ramin Esmzad, Farnaz Adib Yaghmaie, Bahare Kiumarsi +1

This paper introduces a novel direct data-driven control framework based on Natural Gradient Descent (NGD) to design interpretable and robust closed-loop policies without requiring…

eess.SY2026

Consensus and Synchronization of Multi-agent Systems over Finite Fields -- Graph Topologies

Kristian Hengster-Movrić, Šimon Lehký, Farnaz Adib Yaghmaie

This paper brings cooperative protocols for multi-agent systems with agents having a finite state-space. Both scalar single-integrator consensus and general LTI systems synchroniza…

eess.SY2025

Convergence of Flow-Policy Gradient Learning for Linear Quadratic Regulator Problems

Farnaz Adib Yaghmaie, Arunava Naha

Flow -learning has recently been introduced to integrate learning from expert demonstrations into an actor-critic structure. Central to this innovation is the ``the one-step pol…

eess.SY2025

Natural Gradient Descent for Control

Ramin Esmzad, Farnaz Adib Yaghmaie, Hamidreza Modares

This paper bridges optimization and control, and presents a novel closed-loop control framework based on natural gradient descent, offering a trajectory-oriented alternative to tra…

cs.LG20213 cited

A Crash Course on Reinforcement Learning

Farnaz Adib Yaghmaie, Lennart Ljung

The emerging field of Reinforcement Learning (RL) has led to impressive results in varied domains like strategy games, robotics, etc. This handout aims to give a simple introductio…