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

Ahmet Onur Akman, Anastasia Psarou, Łukasz Gorczyca, Zoltán György Varga, Grzegorz Jamróz +1 more
2/27/2025
cs.MAcs.LG

No official code found. Best implementation: COeXISTENCE-PROJECT/RouteRL (42 stars, MIT, last commit 2026-04-22).

Abstract

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.

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Code Implementations(1)

COeXISTENCE-PROJECT/RouteRLCommunity100%
MIT

RouteRL is a multi-agent reinforcement learning framework for modeling and simulating the collective route choices of humans and autonomous vehicles.

4214Python, DockerfileJan 18, 20246 months agoMIT
autonomous-vehiclescavsmulti-agent-reinforcement-learningreinforcement-learningroute-choice+5 more

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Cite this paper

@article{akman2025routerl,
  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},
  year   = {2025},
  eprint = {2502.20065},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2502.20065v1}
}

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