18 citations · 34 across the 9 of their papers we have counts for
9 papers
DeepMPR: Enhancing Opportunistic Routing in Wireless Networks through Multi-Agent Deep Reinforcement Learning
Saeed Kaviani, Bo Ryu, Ejaz Ahmed +4
Opportunistic routing relies on the broadcast capability of wireless networks. It brings higher reliability and robustness in highly dynamic and/or severe environments such as mobi…
DeepADMR: A Deep Learning based Anomaly Detection for MANET Routing
Alex Yahja, Saeed Kaviani, Bo Ryu +2
We developed DeepADMR, a novel neural anomaly detector for the deep reinforcement learning (DRL)-based DeepCQ+ MANET routing policy. The performance of DRL-based algorithms such as…
Deep Reinforcement Learning for System-on-Chip: Myths and Realities
Tegg Taekyong Sung, Bo Ryu
Neural schedulers based on deep reinforcement learning (DRL) have shown considerable potential for solving real-world resource allocation problems, as they have demonstrated signif…
DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networks
Saeed Kaviani, Bo Ryu, Ejaz Ahmed +4
Highly dynamic mobile ad-hoc networks (MANETs) remain as one of the most challenging environments to develop and deploy robust, efficient, and scalable routing protocols. In this p…
A Scalable and Reproducible System-on-Chip Simulation for Reinforcement Learning
Tegg Taekyong Sung, Bo Ryu
Deep Reinforcement Learning (DRL) underlies in a simulated environment and optimizes objective goals. By extending the conventional interaction scheme, this paper proffers gym-ds3,…
SoCRATES: System-on-Chip Resource Adaptive Scheduling using Deep Reinforcement Learning
Tegg Taekyong Sung, Bo Ryu
Deep Reinforcement Learning (DRL) is being increasingly applied to the problem of resource allocation for emerging System-on-Chip (SoC) applications, and has shown remarkable promi…
Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for MANETs
Saeed Kaviani, Bo Ryu, Ejaz Ahmed +4
Highly dynamic mobile ad-hoc networks (MANETs) are continuing to serve as one of the most challenging environments to develop and deploy robust, efficient, and scalable routing pro…
DeepSoCS: A Neural Scheduler for Heterogeneous System-on-Chip (SoC) Resource Scheduling
Tegg Taekyong Sung, Jeongsoo Ha, Jeewoo Kim +3
In this paper, we~present a novel scheduling solution for a class of System-on-Chip (SoC) systems where heterogeneous chip resources (DSP, FPGA, GPU, etc.) must be efficiently sche…
Neural Heterogeneous Scheduler
Tegg Taekyong Sung, Valliappa Chockalingam, Alex Yahja +1
Access to parallel and distributed computation has enabled researchers and developers to improve algorithms and performance in many applications. Recent research has focused on nex…