13 citations · 38 across the 16 of their papers we have counts for
8 papers · 1 filter
Deep reinforcement learning for scheduling in large-scale networked control systems
Adrian Redder, Arunselvan Ramaswamy, Daniel E. Quevedo
This work considers the problem of control and resource scheduling in networked systems. We present DIRA, a Deep reinforcement learning based Iterative Resource Allocation algorith…
Deep Reinforcement Learning for Wireless Sensor Scheduling in Cyber-Physical Systems
Alex S. Leong, Arunselvan Ramaswamy, Daniel E. Quevedo +2
In many Cyber-Physical Systems, we encounter the problem of remote state estimation of geographically distributed and remote physical processes. This paper studies the scheduling o…
DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling
Burak Demirel, Arunselvan Ramaswamy, Daniel E. Quevedo +1
We consider networked control systems consisting of multiple independent controlled subsystems, operating over a shared communication network. Such systems are ubiquitous in cyber-…
Analyzing Approximate Value Iteration Algorithms
Arunselvan Ramaswamy, Shalabh Bhatnagar
In this paper, we consider the stochastic iterative counterpart of the value iteration scheme wherein only noisy and possibly biased approximations of the Bellman operator are avai…
Analysis of gradient descent methods with non-diminishing, bounded errors
Arunselvan Ramaswamy, Shalabh Bhatnagar
The main aim of this paper is to provide an analysis of gradient descent (GD) algorithms with gradient errors that do not necessarily vanish, asymptotically. In particular, suffici…
Stability of Stochastic Approximations with `Controlled Markov' Noise and Temporal Difference Learning
Arunselvan Ramaswamy, Shalabh Bhatnagar
We are interested in understanding stability (almost sure boundedness) of stochastic approximation algorithms (SAs) driven by a `controlled Markov' process. Analyzing this class of…