A Study on Multi-agent Reinforcement Learning for Autonomous Distribution Vehicles

Abstract

A self-driving car, also called an autonomous vehicle, is capable of sensing the environment and driving without any human intervention, utilizing software algorithms and a fully automated driving system. This allows the vehicle to manage external conditions similar to a human. Safe and efficient delivery services require road controls and road restrictions to prevent accidents and reduce damage from unexpected technical failures. This study formulates the Autonomous Delivery Vehicles optimization problem and proposes a multi-agent reinforcement learning method using the analytically calculated shortest path information.