activity
20162022
most citedMulti-agent Reinforcement Learning for Networked System Control

62 citations · 84 across the 10 of their papers we have counts for

collaborators

13 papers

eess.IV2022

An Interactive Annotation Tool for Perceptual Video Compression

Evgenya Pergament, Pulkit Tandon, Kedar Tatwawadi +6

Human perception is at the core of lossy video compression and yet, it is challenging to collect data that is sufficiently dense to drive compression. In perceptual quality assessm…

cs.LG20216 cited

Data Sharing and Compression for Cooperative Networked Control

Jiangnan Cheng, Marco Pavone, Sachin Katti +2

Sharing forecasts of network timeseries data, such as cellular or electricity load patterns, can improve independent control applications ranging from traffic scheduling to power g…

cs.RO2020

Sampling Training Data for Continual Learning Between Robots and the Cloud

Sandeep Chinchali, Evgenya Pergament, Manabu Nakanoya +5

Today's robotic fleets are increasingly measuring high-volume video and LIDAR sensory streams, which can be mined for valuable training data, such as rare scenes of road constructi…

cs.RO20204 cited

Task-relevant Representation Learning for Networked Robotic Perception

Manabu Nakanoya, Sandeep Chinchali, Alexandros Anemogiannis +3

Today, even the most compute-and-power constrained robots can measure complex, high data-rate video and LIDAR sensory streams. Often, such robots, ranging from low-power drones to…

cs.LG2020

Characterizing and Taming Model Instability Across Edge Devices

Eyal Cidon, Evgenya Pergament, Zain Asgar +2

The same machine learning model running on different edge devices may produce highly-divergent outputs on a nearly-identical input. Possible reasons for the divergence include diff…

cs.LG202062 cited

Multi-agent Reinforcement Learning for Networked System Control

Tianshu Chu, Sandeep Chinchali, Sachin Katti

This paper considers multi-agent reinforcement learning (MARL) in networked system control. Specifically, each agent learns a decentralized control policy based on local observatio…