Control of Covid-19 using Agent-based modelling with Reinforcement learning.

Student thesis: Master thesis (including HD thesis)

  • Stergios Polymenidis
  • Bartosz Wawrzyniak
4. term, Control and Automation, Master (Master Programme)
Humanity has been struggling with infectious diseases since the dawn of time.
These diseases pose a great threat to society and the fight against them is sometimes
challenging. Currently, the world is struggling with a pandemic of a virus
that causes a disease called Covid-19. Disease is rarely fatal but spreads quickly
and becomes easily out of control. Controlling the spread of the virus has become
a challenge for governments. In this project, we propose to use a reinforcement
learning algorithm to find the optimal policy to keep hospitalized and severe (requiring
a respirator) cases within the imposed thresholds. We use an agent-based
modelling technique to simulate society and the spread of the virus within it. Following
the actions of governments regarding the pandemics, we have established a
list of policies that, cause reactions similar to those in the real world. We apply the
model-free value iteration reinforcement learning algorithm to the model to find a
sequence of policies that will allow to control the spread of the disease and keep
hospitalized and those requiring a respirator at a level that will not overload health
care. We create three models with different complexities to test the operation of
the algorithm. We simulate two models and the results show that the algorithm
can find the desired sequence of policies.
LanguageEnglish
Publication date3 Jun 2021
Number of pages96
ID: 413670678