Distributed service area control for ride sharing by using multi-agent deep reinforcement learning

Naoki Yoshida, Itsuki Noda, Toshiharu Sugawara

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

We propose a decentralized system to determine where ride-sharing vehicle agents should wait for passengers using multi-agent deep reinforcement learning. Although numerous drivers have begun participating in ride-sharing services as the demand for these services has increased, much of their time is idle. The result is not only inefficiency but also wasted energy and increased traffic congestion in metropolitan area, while also causing a shortage of ride-sharing vehicles in the surrounding areas. We therefore developed the distributed service area adaptation method for ride sharing (dSAAMS) to decide the areas where each agent should wait for passengers through deep reinforcement learning based on the networks of individual agents and the demand prediction data provided by an external system. We evaluated the performance and characteristics of our proposed method in a simulated environment with varied demand occurrence patterns and by using actual data obtained in the Manhattan area. We compare the performance of our method to that of other conventional methods and the centralized version of the dSAAMS. Our experiments indicate that by using the dSAAMS, agents individually wait and move more effectively around their service territory, provide better quality service, and exhibit better performance in dynamically changing environments than when using the comparison methods.

本文言語English
ホスト出版物のタイトルICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence
編集者Ana Paula Rocha, Luc Steels, Jaap van den Herik
出版社SciTePress
ページ101-112
ページ数12
ISBN(電子版)9789897584848
出版ステータスPublished - 2021
イベント13th International Conference on Agents and Artificial Intelligence, ICAART 2021 - Virtual, Online
継続期間: 2021 2 42021 2 6

出版物シリーズ

名前ICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence
1

Conference

Conference13th International Conference on Agents and Artificial Intelligence, ICAART 2021
CityVirtual, Online
Period21/2/421/2/6

ASJC Scopus subject areas

  • 人工知能
  • ソフトウェア

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