Towards Using Deep Reinforcement Learning for Better COVID-19 Vaccine Distribution Strategies

Vaccination has been the most promising hope to get back to normal ever since the COVID-19 outbreak started. But as promising as this sounds, vaccinating all of the population at the same time is practically infeasible because of the limited supply of vaccines from one side and the high demand from the other side. So, the process cannot happen overnight, and this is why governments kept thinking about how they can distribute vaccines in a way that helps their citizens get back to normal with the least possible damages (infections and deaths). In this study, we investigate how Reinforcement Learning (RL) can be used to distribute vaccines more efficiently among the citizens of a country, given their age and profession. For this reason, we created an RL agent that learns vaccine distribution strategies through its interaction with a Monte Carlo (MC) simulation environment that we built. This environment runs an Agent-Based Model (ABM) where we have agents interacting with each other and with the environment where they live and based on their behavior, the virus will spread. The goal of the RL agent was to find vaccine distribution strategies that would minimize the number of infections and deaths in the environment where our agents live. After training our RL agent for 100 episodes, we compared the best strategy that RL gave us with some of the well-known strategies that countries adopt, and we found that the RL strategy outperformed them.