activity
20192022
most citedMulti-agent Reinforcement Learning for Networked System Control

62 citations · 79 across the 7 of their papers we have counts for

collaborators

7 papers

cs.DC20227 cited

A Control Theoretic Approach to Infrastructure-Centric Blockchain Tokenomics

Oguzhan Akcin, Robert P. Streit, Benjamin Oommen +2

There are a multitude of Blockchain-based physical infrastructure systems, operating on a crypto-currency enabled token economy, where infrastructure suppliers are rewarded with to…

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.RO2021

Interpretable Trade-offs Between Robot Task Accuracy and Compute Efficiency

Bineet Ghosh, Sandeep Chinchali, Parasara Sridhar Duggirala

A robot can invoke heterogeneous computation resources such as CPUs, cloud GPU servers, or even human computation for achieving a high-level goal. The problem of invoking an approp…

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.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…