RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehicles
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912250384613376 |
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| author | Akman, Ahmet Onur Psarou, Anastasia Gorczyca, Łukasz Varga, Zoltán György Jamróz, Grzegorz Kucharski, Rafał |
| author_facet | Akman, Ahmet Onur Psarou, Anastasia Gorczyca, Łukasz Varga, Zoltán György Jamróz, Grzegorz Kucharski, Rafał |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20065 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehicles Akman, Ahmet Onur Psarou, Anastasia Gorczyca, Łukasz Varga, Zoltán György Jamróz, Grzegorz Kucharski, Rafał Multiagent Systems Machine Learning 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. |
| title | RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehicles |
| topic | Multiagent Systems Machine Learning |
| url | https://arxiv.org/abs/2502.20065 |