Hierarchical Reinforcement Learning for Scarce Medical Resource Allocation with Imperfect Information-Reference-Cited by-同舟云学术

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Hierarchical Reinforcement Learning for Scarce Medical Resource Allocation with Imperfect Information

Published:2021-08-14 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
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Author:

Hao Qianyue¹,Xu Fengli¹,Chen Lin²,Hui Pan²,Li Yong¹

Affiliation:

1. Tsinghua University, Beijing, China

2. The Hong Kong University of Science and Technology, HongKong, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Beijing Natural Science Foundation

Beijing National Research Center For Information Science And Technology

research fund of Tsinghua University - Tencent Joint Laboratory for Internet Innovation Technology

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3447548.3467181

Reference28 articles.

1. Principles of Scarce Medical Resource Allocation in Natural Disaster Relief

2. To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic

3. Fair Allocation of Scarce Medical Resources in the Time of Covid-19

4. Preventing epidemics by wearing masks: An application to COVID-19

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1. DyPS: Dynamic Parameter Sharing in Multi-Agent Reinforcement Learning for Spatio-Temporal Resource Allocation;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

2. Data-driven hospitals staff and resources allocation using agent-based simulation and deep reinforcement learning;Engineering Applications of Artificial Intelligence;2023-11

3. HRL4EC: Hierarchical reinforcement learning for multi-mode epidemic control;Information Sciences;2023-09

4. GAT-MF: Graph Attention Mean Field for Very Large Scale Multi-Agent Reinforcement Learning;Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2023-08-04

5. A Predict-Then-Optimize Couriers Allocation Framework for Emergency Last-mile Logistics;Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2023-08-04

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