In this work we present an advanced deep multiagent reinforcement learning scheme for autonomous driving of multiple vehicles in traffic networks with road segments and unsignaled intersections. The key aspect of the proposed method lies on the definition of the possible routes one vehicle can follow as agents, called route-agents, that allows the transfer learning and the reuse of their learned policies. An informative state space is constructed by introducing a collision matrix that “displays” a predictive traffic map of the intersections that the vehicles traverse. Also, an efficient reward function is designed that aims at providing optimal driving policies in order to navigate vehicles safely and rapidly to their destinations, avoiding collisions. A combined scheme of a Deep Q-Network (DQN) and a feedforward neural network along with the Double Q-leaning technique is employed that offers more stable solutions. Several experiments were made in simulated road traffic maps that empirically illustrate the efficiency of the proposed deep multiagent framework.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Double deep multiagent reinforcement learning for autonomous driving in traffic maps with road segments and unsignaled intersections


    Beteiligte:


    Erscheinungsdatum :

    2020-09-20


    Format / Umfang :

    888269 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Multiagent reinforcement learning for autonomous driving in traffic zones with unsignalized intersections

    Spatharis, Christos / Blekas, Konstantinos | Taylor & Francis Verlag | 2024


    Safe and Rule-Aware Deep Reinforcement Learning for Autonomous Driving at Intersections

    Zhang, Chi / Kacem, Kais / Hinz, Gereon et al. | IEEE | 2022



    Autonomous Driving with Deep Reinforcement Learning

    Zhu, Yuhua / Technische Universität Dresden | SLUB | 2023


    Deep reinforcement‐learning‐based driving policy for autonomous road vehicles

    Makantasis, Konstantinos / Kontorinaki, Maria / Nikolos, Ioannis | Wiley | 2020

    Freier Zugriff