Deep Reinforcement Learning-based Vaccine Distribution Strategies

The rapid development of artificial intelligence techniques is significantly promoting the resolution of various important decision-making issues such as material distribution, generation line optimization scheduling, and path planning. Currently, SARS-CoV-2 is raging over the world, and it is valuable to propose a vaccine distribution strategy to utilize limited vaccine resources rationally. In this paper, we aim to propose an optimal vaccine distribution strategy based on deep reinforcement learning(DRL) approaches. An End-to-End vaccine distribution model is proposed by combining the Deep Reinforcement Learning model and LinUCB algorithm to get an optimistic strategy of allocation. Experiment results demonstrated that vaccine distribution strategies based on this model show a strong capacity to control the epidemic and ensure stable government revenue compared with baseline strategies.