Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach

2020 ◽  
Author(s):  
Yiwei Sun ◽  
Suhang Wang ◽  
Xianfeng Tang ◽  
Tsung-Yu Hsieh ◽  
Vasant Honavar
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Learning Approach ◽  
Hierarchical Reinforcement Learning ◽  
Graph Neural Networks

scholarly journals ReLeQ : A Reinforcement Learning Approach for Automatic Deep Quantization of Neural Networks

IEEE Micro ◽  
2020 ◽  
Vol 40 (5) ◽  
pp. 37-45 ◽  
Author(s):  
Ahmed T. Elthakeb ◽  
Prannoy Pilligundla ◽  
Fatemehsadat Mireshghallah ◽  
Amir Yazdanbakhsh ◽  
Hadi Esmaeilzadeh
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Learning Approach

Combining Deep Reinforcement Learning With Graph Neural Networks for Optimal VNF Placement

2021 ◽  
Vol 25 (1) ◽  
pp. 176-180
Author(s):  
Penghao Sun ◽  
Julong Lan ◽  
Junfei Li ◽  
Zehua Guo ◽  
Yuxiang Hu
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Graph Neural Networks

scholarly journals Real-Time Object Navigation With Deep Neural Networks and Hierarchical Reinforcement Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 195608-195621
Author(s):  
Aleksey Staroverov ◽  
Dmitry A. Yudin ◽  
Ilya Belkin ◽  
Vasily Adeshkin ◽  
Yaroslav K. Solomentsev ◽  
...  
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Real Time ◽  
Deep Neural Networks ◽  
Hierarchical Reinforcement Learning

scholarly journals On the Design of Tailored Neural Networks for Energy Harvesting Broadcast Channels: A Reinforcement Learning Approach

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 179678-179691
Author(s):  
Heasung Kim ◽  
Jungtai Kim ◽  
Wonjae Shin ◽  
Heecheol Yang ◽  
Nayoung Lee ◽  
...  
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Energy Harvesting ◽  
Broadcast Channels ◽  
Learning Approach

scholarly journals Creation and implementation of a set of game strategies based on training neural networks with reinforcement learning

2021 ◽  
Vol 2134 (1) ◽  
pp. 012005
Author(s):  
D S Kozlov ◽  
O N Polovikova
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
The Self ◽  
Learning Approach ◽  
Self Learning

Abstract The study explores the problems of reinforcement learning and finding non-obvious play strategies using reinforcement learning. Two approaches to agent training (blind and pattern-based) are considered and implemented. The advantage of the self-learning approach with reinforcement using patterns as applied to a specific game (tic-tac-toe five in a row) is shown. Recorded and analyzed the use of unusual strategies by an agent using a pattern-based approach.

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