RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehicles

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{akman2025_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 }
}