Truck-Drone Delivery Optimization Based on Multi-Agent Reinforcement Learning

In recent years, the adoption of truck-drone collaborative delivery has emerged as an innovative approach to enhance transportation efficiency and minimize the depletion of human resources. Such a model simultaneously addresses the endurance limitations of drones and the time wastage incurred during the "last-mile" deliveries by trucks. Trucks serve not only as a carrier platform for drones but also as storage hubs and energy sources for these unmanned aerial vehicles. Drawing from the distinctive attributes of truck-drone collaborative delivery, this research has created a multi-drone delivery environment utilizing the MPE library. Furthermore, a spectrum of optimization techniques has been employed to enhance the algorithm's efficacy within the truck-drone distribution system. Finally, a comparative analysis is conducted with other multi-agent reinforcement learning algorithms within the same environment, thus affirming the rationality of the problem formulation and highlighting the algorithm's superior performance.