A study on multi-agent reinforcement learning for autonomous distribution vehicles

Abstract

A self-driving automobile, also known as an autonomous vehicle, can observe its surroundings and maneuver without the assistance of a driver thanks to software algorithms and a completely automated driving system. As a result, the car may react to the environment in a way that is comparable to a human driver. Road regulations and limitations are required to guarantee the security and effectiveness of delivery services, as well as to stop accidents and damage brought on by technology failures. This paper formulates the Autonomous Delivery Vehicles optimization problem and proposes a Multi-Agent Reinforcement Learning approach that makes use of shortest-path data that have been obtained analytically. This method allows for the training of multiple agents to work together and improve delivery. Using this strategy, autonomous delivery vehicles' efficiency and security can be markedly enhanced.