| dc.description.abstract | Coronavirus Disease 2019 (COVID-19) has spread rapidly around the world since the end of 2019. As a result, the demand for epidemic prevention materials (e.g., medical-grade masks) has increased drastically. If the masks are not properly controlled, it will lead to understock and price gouging. In Taiwan, since the very early stage of pandemic, the medical-grade masks have been collected and managed by the government, and have been sold to all residents for a fixed price. In this case, the supply chain optimization becomes an important issue. For instance, if the government allocates too many masks to a region, the residents in other regions may suffer from resource shortage. It is crucial that the masks are distributed to each region in the amount close to the daily consumption for efficient COVID-19 prevention. In this study, we propose a robust system for the allocation of medical-grade masks. The proposed system adopts the reinforcement learning framework, which takes the daily supply and demand of masks as the environment, the 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, and the results indicate that the proposed system achieves more rewards in the environment. Moreover, our reinforcement learning framework has a consistent performance under different total numbers of masks. | en_US |