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

• URL: http://arxiv.org/abs/2502.20065v1
• Date: Thu, 27 Feb 2025 13:13:09 GMT
• Title: RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehicles
• Authors: Ahmet Onur Akman, Anastasia Psarou, Łukasz Gorczyca, Zoltán György Varga, Grzegorz Jamróz, Rafał Kucharski,
• Abstract summary: RouteRL is a novel framework that integrates multi-agent reinforcement learning (MARL) with a microscopic traffic simulation.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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