Real-Time Multi-Vehicle Scheduling in Tasks With Dependency Relationships Using Multi-Agent Reinforcement Learning

With the advancement of technology in vehicle-road collaboration and autonomous driving, new commercial applications have surfaced. These include autonomous ride-hailing vehicles and unmanned delivery vehicles. As a result of the challenges presented by commercial applications, dispatching systems are moving towards being maintenance-free, centralized, multitasking, and real-time. Yet, most existing dispatching systems have been designed for single-task purposes and cannot tackle multitasking issues. Moreover, traditional optimization algorithms make it difficult to achieve timeliness in real-time changing traffic conditions. Therefore, this paper innovatively proposes a task allocation method based on Multi-Agent Reinforcement Learning (MARL). Firstly, this study introduces a classification model of task relationships through the binary assumption model of geographical areas and vehicles. Secondly, the study matches the classification model’s task cost state transition process with the Markov Decision Process, constructing a Multi-Agent Reinforcement Learning framework. Finally, the study constructs a simulation environment suitable for reinforcement learning based on Simulation of Urban Mobility (SUMO). Simulation results indicate that the task allocation system based on MARL can effectively improve the system’s overall efficiency by determining the order of task allocation and the matching relationships between tasks.