no code implementations • 11 Jul 2023 • Yue Tian, Guanjun Liu
Therefore, we propose a novel heterogeneous graph neural network called Spatial-Temporal-Aware Graph Transformer (STA-GT) for transaction fraud detection problems.
no code implementations • 11 Jul 2023 • Yue Tian, Guanjun Liu, Jiacun Wang, Mengchu Zhou
A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes.
no code implementations • 3 Jul 2023 • Weiran Guo, Guanjun Liu, Ziyuan Zhou, Ling Wang, Jiacun Wang
To increase the robustness of multi-agent reinforcement learning (MARL) algorithms, we train models using a variety of attacks in this research.
no code implementations • 9 Jun 2023 • Ziyuan Zhou, Guanjun Liu
Then, adversarial state perturbations of the critical agents are generated based on the worst-case joint actions.
no code implementations • 17 May 2023 • Ziyuan Zhou, Guanjun Liu, Ying Tang
This paper aims to review methods and applications and point out research trends and visionary prospects for the next decade.
no code implementations • 25 Apr 2023 • Min Yang, Guanjun Liu, Ziyuan Zhou
In this paper, we propose a novel multi-agent reinforcement learning algorithm, Partially Observable Mean Field Multi-Agent Reinforcement Learning based on Graph--Attention (GAMFQ) to remedy this flaw.
no code implementations • 15 May 2022 • Ziyuan Zhou, Guanjun Liu
Multi-agent deep reinforcement learning makes optimal decisions dependent on system states observed by agents, but any uncertainty on the observations may mislead agents to take wrong actions.