GAT-MF: Graph Attention Mean Field for Very Large Scale Multi-Agent Reinforcement Learning-Reference-Cited by-同舟云学术

GAT-MF: Graph Attention Mean Field for Very Large Scale Multi-Agent Reinforcement Learning

Published:2023-08-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Hao Qianyue¹^ORCID,Huang Wenzhen¹^ORCID,Feng Tao¹^ORCID,Yuan Jian²^ORCID,Li Yong¹^ORCID

Affiliation:

1. Tsinghua University, Beijing, China

2. Tsinghua University, Tsinghua University, Beijing, China

Funder

National Natural Science Foundation of China

Beijing National Research Center For Information Science And Technology

The National Key Research and Development Program of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599359

Reference56 articles.

1. Reliability-aware: task scheduling in cloud computing using multi-agent reinforcement learning algorithm and neural fitted Q;Balla Husamelddin AM;Int. Arab J. Inf. Technol.,2021

2. Hamsa Bastani , Kimon Drakopoulos , Vishal Gupta , Ioannis Vlachogiannis , Christos Hadjicristodoulou , Pagona Lagiou , Gkikas Magiorkinis , Dimitrios Paraskevis , and Sotirios Tsiodras . 2021 . Efficient and targeted COVID-19 border testing via reinforcement learning . Nature , Vol. 599 , 7883 (2021), 108--113. Hamsa Bastani, Kimon Drakopoulos, Vishal Gupta, Ioannis Vlachogiannis, Christos Hadjicristodoulou, Pagona Lagiou, Gkikas Magiorkinis, Dimitrios Paraskevis, and Sotirios Tsiodras. 2021. Efficient and targeted COVID-19 border testing via reinforcement learning. Nature, Vol. 599, 7883 (2021), 108--113.

3. Wendelin Böhmer , Vitaly Kurin , and Shimon Whiteson . 2020 . Deep coordination graphs . In International Conference on Machine Learning. PMLR, 980--991 . Wendelin Böhmer, Vitaly Kurin, and Shimon Whiteson. 2020. Deep coordination graphs. In International Conference on Machine Learning. PMLR, 980--991.

4. Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning

5. Serina Chang , Emma Pierson , Pang Wei Koh , Jaline Gerardin, Beth Redbird, David Grusky, and Jure Leskovec. 2021 a. Mobility network models of COVID-19 explain inequities and inform reopening. Nature , Vol. 589 , 7840 (2021), 82--87. Serina Chang, Emma Pierson, Pang Wei Koh, Jaline Gerardin, Beth Redbird, David Grusky, and Jure Leskovec. 2021a. Mobility network models of COVID-19 explain inequities and inform reopening. Nature, Vol. 589, 7840 (2021), 82--87.

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