Innovating Logistics with Synthetic Datasets

This chapter explores the creation of a synthetic dataset for training reinforcement learning models using data from New York City's taxi and limousine commission alongside OpenStreetMap. It highlights the development of a custom simulation engine for logistics, the implementation of advanced technologies, and the intricacies of model training in varied environments, including transfer learning applications. Additionally, the discussion emphasizes the balance between learning-based solutions and heuristic methods to enhance efficiency while addressing challenges in explainability and robustness.