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Abstract
This article explores the use of reinforcement learning to determine optimal actions for controlling the spread of a pandemic while considering economic factors. A virtual pandemic scenario, similar to the COVID-19 crisis, is created, and a reinforcement learning agent is trained to find optimal strategies for minimizing disease spread and maintaining economic stability. The agent's actions, including lockdown lengths and timing, are analyzed to understand the decision-making process.
Publisher
Scientific Reports
Published On
Dec 16, 2020
Authors
Abu Quwsar Ohi, M. F. Mridha, Muhammad Mostafa Monowar, Md. Abdul Hamid
Tags
reinforcement learning
pandemic control
economic stability
disease spread
lockdown strategies

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