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Authors: BumKyu Choi ; Jean Seong Bjorn Choe and Jong-kook Kim

Affiliation: School of Electrical Engineering, Korea University, An-am Street, Seoul, South Korea

Keyword(s): Multi-agent, Reinforcement Learning, Traffic Control, Traffic Intersection Management.

Abstract: Traffic congestion is increasing steadily worldwide and many researchers have attempted to employ smart methods to control the traffic. One such approach is the multi-agent reinforcement learning (MARL) scheme wherein each agent corresponds to a moving entity such as vehicles. The aim is to make all mobile objects arrive at their target destination in the least amount of time without collision. However, as the number of vehicles increases, the computational complexity increases, and therefore computation cost increases, and scalability cannot be guaranteed. In this paper, we propose a novel approach using MARL, where the traffic junction becomes the agent. Each traffic junction is composed of four Micro Junction Agents (MJAs) and a MJA becomes the observer and the agent controlling all vehicles within the observation area. Results show that MJA outperforms other MARL techniques on various traffic junction scenarios.

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Paper citation in several formats:
Choi, B. ; Choe, J. and Kim, J. (2022). Micro Junction Agent: A Scalable Multi-agent Reinforcement Learning Method for Traffic Control. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 509-515. DOI: 10.5220/0010849600003116

@conference{icaart22,
author={BumKyu Choi and Jean Seong Bjorn Choe and Jong{-}kook Kim},
title={Micro Junction Agent: A Scalable Multi-agent Reinforcement Learning Method for Traffic Control},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2022},
pages={509-515},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010849600003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Micro Junction Agent: A Scalable Multi-agent Reinforcement Learning Method for Traffic Control
SN - 978-989-758-547-0
IS - 2184-433X
AU - Choi, B.
AU - Choe, J.
AU - Kim, J.
PY - 2022
SP - 509
EP - 515
DO - 10.5220/0010849600003116
PB - SciTePress

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