Dueling Network Architecture for GNN in the Deep Reinforcement Learning for the Automated ICT System Design

This paper presents an improved deep reinforcement learning-based (DRL) approach for end-to-end models using a Graph Neural Network(GNN). The proposed method aims to improve end-to-end deep Q learning with a GNN by decomposing the GNN-based Q-network structure into two sub-streams to separately esti...

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Bibliographic Details
Main Authors:	Tianchen Zhou, Yutaka Yakuwa, Natsuki Okamura, Hiroyuki Hochigai, Takayuki Kuroda, Ikuko Eguchi Yairi
Format:	Article
Language:	English
Published:	IEEE 2025-01-01
Series:	IEEE Access
Subjects:	Machine learning deep reinforcement learning system design design automation
Online Access:	https://ieeexplore.ieee.org/document/10854435/
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Dueling Network Architecture for GNN in the Deep Reinforcement Learning for the Automated ICT System Design

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