RouteRL is a novel framework that integrates multi-agent reinforcement learning (MARL) with a microscopic traffic simulation, facilitating the testing and development of efficient route choice strategies for autonomous vehicles (AVs). The proposed framework simulates the daily route choices of driver agents in a city, including two types: human drivers, emulated using behavioral route choice models, and AVs, modeled as MARL agents optimizing their policies for a predefined objective. RouteRL aims to advance research in MARL, transport modeling, and human-AI interaction for transportation applications. This study presents a technical report on RouteRL, outlines its potential research contributions, and showcases its impact via illustrative examples.
@article{arxiv.2502.20065,
title = {RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehicles},
author = {Ahmet Onur Akman and Anastasia Psarou and Łukasz Gorczyca and Zoltán György Varga and Grzegorz Jamróz and Rafał Kucharski},
journal= {arXiv preprint arXiv:2502.20065},
year = {2025}
}