Jun 28, 2022 · In this paper, we study the batch active learning setting for GNNs where the learning agent can acquire labels of multiple samples at each time.
We formulate batch active learning as a cooperative multi-agent reinforcement learning problem. Suppose we want to select n samples in each iteration, then ...
Apr 1, 2022 · Specifically, we introduce a hierarchical Q-learning network to manipulate the labels of the adversarial nodes and their links with other nodes ...
This collection of papers can be used to summarize research about graph reinforcement learning for the convenience of researchers.
Batch Active Learning with Graph Neural Networks via Multi-Agent Deep Reinforcement Learning. Yuheng Zhang, Hanghang Tong, Yinglong Xia, Yan Zhu, Yuejie Chi ...
How To Learn From Graph Data With ... [AAAI'2022-BIGENE] Batch active learning with graph neural networks via multi-agent deep reinforcement learning.
Oct 21, 2024 · Batch active learning with graph neural networks via multi-agent deep reinforcement learning. In AAAI, Vol. 36. 9118--9126. Crossref · Google ...
Mar 19, 2024 · We present a graph neural network (GNN)-based multi-agent reinforcement learning (MARL) method for resilient distributed coordination of a multi-robot system.
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Jan 20, 2023 · GQN uses graph neural networks to generalize to different network topologies and share knowledge across neighboring base stations and different.
Batch active learning with graph neural networks via multi-agent deep reinforcement learning. Proceedings of the AAAI Conference on Artificial Intelligence ...