A deep reinforcement learning system for the allocation of epidemic prevention materials based on DDPG

COVID-19 has spread rapidly around the world since the end of 2019. Consequently, the demand for epidemic prevention materials (e.g., medical-grade masks) has increased drastically. The medical-grade masks have become a necessary item for everyone. Referring to the shortage of medical-grade masks in Taiwan, the government collected and managed them at the early stage and sold them for a fixed price. However, the government must distribute masks around the country to prevent the movement of people who can cause infection with COVID-19. Moreover, the demands for medical supplies during the pandemic have become more complex and dynamic. This study proposes a robust system for allocating epidemic prevention materials. The proposed approach adopts the reinforcement learning framework, which takes the daily supply and demand for masks as the environment, with the Deep Deterministic Policy Gradient (DDPG) algorithm for agent updates and the daily shortage as rewards and punishments. The proposed system is compared with the traditional machine learning approach used for supply chain demand forecasting through experiments. The results indicate that our proposed method is superior regarding the number of pharmacies with over-stocked masks and rewards. Moreover, our proposed system performs consistently under different total numbers of masks.