Multi-Agent Reinforcement Learning for Autonomous Vehicle Coordination

   Info_icon.svg       BibTeX_logo.svg   
@courseproject{autveicmas2024-mas2324,
    abstract = {The proposed project focuses on the development of a system where autonomous vehicles are modeled as agents, using Multi-Agent Reinforcement Learning (MARL) to coordinate their actions. The goal is to optimize traffic flow, minimize travel time, and avoid collisions. The system will be evaluated in a simulated environment, measuring key performance metrics such as traffic efficiency and safety. This project will combine the areas of multi-agent systems, reinforcement learning, and autonomous driving to produce a scalable solution for real-world traffic management.},
    apice = {AutveicMas2024},
    author = {Conca, Edoardo},
    howpublished = {Multi-Agent Systems, A.Y. 2023/2024},
    month = mar,
    sort = {classic},
    supervisor = {Calegari, Roberta},
    title = {Multi-Agent Reinforcement Learning for Autonomous Vehicle Coordination},
    type = {Standard Project},
    url = {https://dvcs.apice.unibo.it/pika-lab/courses/mas/projects/mas-project-conca-2324},
    year = 2025
}