Optimal control problem of various epidemic models with uncertainty based on deep reinforcement learning

Yoon gu Hwang, Hee Dae Kwon, Jeehyun Lee

Research output: Contribution to journalArticlepeer-review


We investigate an optimal control problem of various epidemic models with uncertainty using stochastic differential equations, random differential equations, and agent-based models. We discuss deep reinforcement learning (RL), which combines RL with deep neural networks, as one method to solve the optimal control problem. The deep Q-network algorithm is introduced to approximate an action-value function and consequently obtain the optimal policy. Numerical simulations show that in order to effectively prevent the spread of infectious diseases, it is essential to vaccinate at the highest rate for the first few days and then gradually reduce the rate.

Original languageEnglish
Pages (from-to)2142-2162
Number of pages21
JournalNumerical Methods for Partial Differential Equations
Issue number6
Publication statusPublished - 2022 Nov

Bibliographical note

Funding Information:
The work of Hee‐Dae Kwon was supported by NRF‐2016R1D1A1B04931897 and NRF‐2021R1A2C1009878. The work of Jeehyun Lee was supported by government‐wide R&D Fund project for infectious disease research through GFID, funded by seven ministries (grant number: HG18C0000) and 2020R1A2C1A0101077511.

Funding Information:
National Research Foundation of Korea, 2016R1D1A1B04931897; 2020R1A2C1A0101077511; 2021R1A2C1009878; HG18C0000 Funding information

Publisher Copyright:
© 2022 Wiley Periodicals LLC.

All Science Journal Classification (ASJC) codes

  • Analysis
  • Numerical Analysis
  • Computational Mathematics
  • Applied Mathematics


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