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

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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