Multiagent Reinforcement Learning With Graphical Mutual Information Maximization

Ding, Shifei; Du, Wei; Ding, Ling; Zhang, Jian; Guo, Lili; An, Bo

doi:10.1109/tnnls.2023.3243557

Cited by 3 publications

(1 citation statement)

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“…To solve this problem, Ding et al [47] presented a method to extract useful information from neighboring agents as much as possible in the graph structure, which has the ability to provide feature representation to complete the cooperation task. For this purpose, mutual information (MI) is applied for measuring the agent topological relationships and the agent features information to maximize the correlation between input feature information of neighbor agents and output high-level hidden feature representations.…”

Section: Interaction Methods Between Multi-agents With Gnn Architecturementioning

confidence: 99%

Graph Neural Networks and Reinforcement Learning: A Survey

Fathinezhad¹,

Adibi²,

Shoushtarian³

et al. 2023

Deep Learning and Reinforcement Learning

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Graph neural network (GNN) is an emerging field of research that tries to generalize deep learning architectures to work with non-Euclidean data. Nowadays, combining deep reinforcement learning (DRL) with GNN for graph-structured problems, especially in multi-agent environments, is a powerful technique in modern deep learning. From the computational point of view, multi-agent environments are inherently complex, because future rewards depend on the joint actions of multiple agents. This chapter tries to examine different types of applying GNN and DRL techniques in the most common representations of multi-agent problems and their challenges. In general, the fusion of GNN and DRL can be addressed from two different points of view. First, GNN is used to influence the DRL performance and improve its formulation. Here, GNN is applied in relational DRL structures such as multi-agent and multi-task DRL. Second, DRL is used to improve the application of GNN. From this viewpoint, DRL can be used for a variety of purposes including neural architecture search and improving the explanatory power of GNN predictions.

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Section: Interaction Methods Between Multi-agents With Gnn Architecturementioning

confidence: 99%