6 papers
The case for and against fixed step-size: Stochastic approximation algorithms in optimization and machine learning
Caio Kalil Lauand, Ioannis Kontoyiannis, Sean Meyn
Theory and application of stochastic approximation (SA) have become increasingly relevant due in part to applications in optimization and reinforcement learning. This paper takes a…
Interacting Particle Systems for Fast Linear Quadratic RL
Anant A Joshi, Heng-Sheng Chang, Amirhossein Taghvaei +2
This paper is concerned with the design of algorithms based on systems of interacting particles to represent, approximate, and learn the optimal control law for reinforcement learn…
Moment Constrained Optimal Transport for Control Applications
Thomas Le Corre, Ana Busic, Sean Meyn
This paper concerns the application of techniques from optimal transport (OT) to mean field control, in which the probability measures of interest in OT correspond to empirical dis…
Functional role of synchronization: A mean-field control perspective
Prashant Mehta, Sean Meyn
The broad goal of the research surveyed in this article is to develop methods for understanding the aggregate behavior of interconnected dynamical systems, as found in mathematical…
Dual Ensemble Kalman Filter for Stochastic Optimal Control
Anant A. Joshi, Amirhossein Taghvaei, Prashant G. Mehta +1
In this paper, stochastic optimal control problems in continuous time and space are considered. In recent years, such problems have received renewed attention from the lens of rein…
Lecture Notes on Control System Theory and Design
Tamer Basar, Sean Meyn, William R. Perkins
This is a collection of the lecture notes of the three authors for a first-year graduate course on control system theory and design (ECE 515 , formerly ECE 415) at the ECE Departme…