Data-driven control of COVID-19 in buildings: a reinforcement-learning approach
In addition to its public health crisis, COVID-19 pandemic has led to the shutdown and closure of workplaces with an estimated total cost of more than $16 trillion. Given the long hours an average person spends in buildings and indoor environments, this research article proposes data-driven control...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
27.12.2022
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Subjects | |
Online Access | Get full text |
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Summary: | In addition to its public health crisis, COVID-19 pandemic has led to the
shutdown and closure of workplaces with an estimated total cost of more than
$16 trillion. Given the long hours an average person spends in buildings and
indoor environments, this research article proposes data-driven control
strategies to design optimal indoor airflow to minimize the exposure of
occupants to viral pathogens in built environments. A general control framework
is put forward for designing an optimal velocity field and proximal policy
optimization, a reinforcement learning algorithm is employed to solve the
control problem in a data-driven fashion. The same framework is used for
optimal placement of disinfectants to neutralize the viral pathogens as an
alternative to the airflow design when the latter is practically infeasible or
hard to implement. We show, via simulation experiments, that the control agent
learns the optimal policy in both scenarios within a reasonable time. The
proposed data-driven control framework in this study will have significant
societal and economic benefits by setting the foundation for an improved
methodology in designing case-specific infection control guidelines that can be
realized by affordable ventilation devices and disinfectants. |
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DOI: | 10.48550/arxiv.2212.13559 |