Reinforcement Learning for Optimizing Delivery Paths in Hospital Settings: A Review

Abstract

Reinforcement learning is a branch of machine learning that facilitates the interaction of autonomous agents with their environments. This is done by “teaching” an agent efficient decision-making through iterative processes of exploration and trial-and-error. This review article focuses on the application of reinforcement learning within the healthcare industry. We review recent publications that address the optimization of the pickup and delivery processes for essential supplies and medications with mobile robots, and the integration of these two key technologies to improve hospital operations efficiency. We also investigate the gap between research results and real-world applications, and point out directions for future work.