QMIX-GNN: A Graph Neural Network-Based Heterogeneous Multi-Agent Reinforcement Learning Model for Improved Collaboration and Decision-Making

QMIX-GNN: A Graph Neural Network-Based Heterogeneous Multi-Agent Reinforcement Learning Model for Improved Collaboration and Decision-Making

In multi-agent reinforcement learning, the fully centralized approach suffers from issues such as explosion of the joint state and action spaces, leading to performance degradation. On the other hand, the fully decentralized approach relies on agents that focus solely on maximizing their own rewards...

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Bibliographic Details
Main Authors: Taiyin Zhao, Tian Chen, Bing Zhang
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Applied Sciences
Subjects:
deep learning
reinforcement learning
multi-agent reinforcement learning
graph neural network
heterogeneity
Online Access:https://www.mdpi.com/2076-3417/15/7/3794
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