A Data-driven Spatiotemporal Simulator for Reinforcement Learning Methods

Spatiotemporal applications such as taxi order dispatching and warehouse task scheduling depend critically on the algorithms for operational efficiency. However, the inherent dynamic nature of these applications presents challenges in algorithm design. The growth of mobility services has facilitated the collection of extensive spatiotemporal data, which in turn prompted algorithm designers to use data-driven methods. Reinforcement learning (RL), recognized for its strong performance and suitability for spatiotemporal contexts, has garnered considerable research interest. Despite their potential, RL algorithms necessitate the use of a simulator for both training and validation purposes. However, no specific simulation system has been developed for spatiotemporal algorithm design. This vacancy hinders the progress of spatiotemporal algorithm designers. In this demo, we build a system called Data-driven Spatiotemporal Simulator (DSS), hoping to bring convenience for spa-tiotemporal algorithm designers. DSS is adept at handling problems related to taxi order dispatching and warehouse task scheduling and possesses the versatility to be expanded for other user-defined scenarios. The system includes visualization modules that offer insightful panels, alongside developer tools designed to streamline the development process. This enables designers to efficiently craft, evaluate, and refine their algorithms, potentially accelerating innovation in spatiotemporal application development.