13 citations · 38 across the 16 of their papers we have counts for
4 papers · 1 filter
Multi-Stage Reinforcement Learning For Object Detection
Jonas Koenig, Simon Malberg, Martin Martens +3
We present a reinforcement learning approach for detecting objects within an image. Our approach performs a step-wise deformation of a bounding box with the goal of tightly framing…
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-…
Asynchronous stochastic approximations with asymptotically biased errors and deep multi-agent learning
Arunselvan Ramaswamy, Shalabh Bhatnagar, Daniel E. Quevedo
Asynchronous stochastic approximations (SAs) are an important class of model-free algorithms, tools and techniques that are popular in multi-agent and distributed control scenarios…