Cooperative Multiagent Deep Deterministic Policy Gradient (CoMADDPG) for Intelligent Connected Transportation with Unsignalized Intersection
المؤلفون المشاركون
Zhang, Lin
Wu, Tianhao
Jiang, Mingzhi
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-07-22
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
Unsignalized intersection control is one of the most critical issues in intelligent transportation systems, which requires connected and automated vehicles to support more frequent information interaction and on-board computing.
It is very promising to introduce reinforcement learning in the unsignalized intersection control.
However, the existing multiagent reinforcement learning algorithms, such as multiagent deep deterministic policy gradient (MADDPG), hardly handle a dynamic number of vehicles, which cannot meet the need of the real road condition.
Thus, this paper proposes a Cooperative MADDPG (CoMADDPG) for connected vehicles at unsignalized intersection to solve this problem.
Firstly, the scenario of multiple vehicles passing through an unsignalized intersection is formulated as a multiagent reinforcement learning (RL) problem.
Secondly, MADDPG is redefined to adapt to the dynamic quantity agents, where each vehicle selects reference vehicles to construct a partial stationary environment, which is necessary for RL.
Thirdly, this paper incorporates a novel vehicle selection method, which projects the reference vehicles on a virtual lane and selects the largest impact vehicles to construct the environment.
At last, an intersection simulation platform is developed to evaluate the proposed method.
According to the simulation result, CoMADDPG can reduce average travel time by 39.28% compared with the other optimization-based methods, which indicates that CoMADDPG has an excellent prospect in dealing with the scenario of unsignalized intersection control.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wu, Tianhao& Jiang, Mingzhi& Zhang, Lin. 2020. Cooperative Multiagent Deep Deterministic Policy Gradient (CoMADDPG) for Intelligent Connected Transportation with Unsignalized Intersection. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1193552
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wu, Tianhao…[et al.]. Cooperative Multiagent Deep Deterministic Policy Gradient (CoMADDPG) for Intelligent Connected Transportation with Unsignalized Intersection. Mathematical Problems in Engineering No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1193552
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wu, Tianhao& Jiang, Mingzhi& Zhang, Lin. Cooperative Multiagent Deep Deterministic Policy Gradient (CoMADDPG) for Intelligent Connected Transportation with Unsignalized Intersection. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1193552
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1193552
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر