  Pandemics involve the high transmission of a disease that impacts global and
local health and economic patterns. The impact of a pandemic can be minimized
by enforcing certain restrictions on a community. However, while minimizing
infection and death rates, these restrictions can also lead to economic crises.
Epidemiological models help propose pandemic control strategies based on
non-pharmaceutical interventions such as social distancing, curfews, and
lockdowns, reducing the economic impact of these restrictions. However,
designing manual control strategies while considering disease spread and
economic status is non-trivial. Optimal strategies can be designed through
multi-objective reinforcement learning (MORL) models, which demonstrate how
restrictions can be used to optimize the outcome of a pandemic. In this
research, we utilized an epidemiological Susceptible, Exposed, Infected,
Recovered, Deceased (SEIRD) model: a compartmental model for virtually
simulating a pandemic day by day. We combined the SEIRD model with a deep
double recurrent Q-network to train a reinforcement learning agent to enforce
the optimal restriction on the SEIRD simulation based on a reward function. We
tested two agents with unique reward functions and pandemic goals to obtain two
strategies. The first agent placed long lockdowns to reduce the initial spread
of the disease, followed by cyclical and shorter lockdowns to mitigate the
resurgence of the disease. The second agent provided similar infection rates
but an improved economy by implementing a 10-day lockdown and 20-day
no-restriction cycle. This use of reinforcement learning and epidemiological
modeling allowed for both economic and infection mitigation in multiple
pandemic scenarios.
