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Optimal Resource Allocation during Epidemics using Reinforcement Learning
1st International Conference on Informatics, ICI 2022 ; : 98-102, 2022.
Article in English | Scopus | ID: covidwho-1932109
ABSTRACT
Epidemics can prove to be disastrous, which has been further emphasized by the recent COVID-19 pandemic, and several countries like India lack sufficient resources to meet the population's needs. It is therefore important that the limited testing and protective resources are utilized such that the disease spread is minimized and their reach to the most vulnerable demographic is maximized. This paper studies the scope of intelligent agents in aiding authorities with such policy-making decisions. This is done by exploring the performance of various action selection methods on custom environments dealing with socio-economic groups and Indian states. Experiments using multi-armed bandit techniques provide greater insight into administrative decisions surrounding resource allocation and their future potential for greater use in similar scenarios. © 2022 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies / Randomized controlled trials Language: English Journal: 1st International Conference on Informatics, ICI 2022 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies / Randomized controlled trials Language: English Journal: 1st International Conference on Informatics, ICI 2022 Year: 2022 Document Type: Article