In this paper, a multi-objective deep Q-learning (MOM-DQL) method is developed for solving eco- routing problem in a signalized traffic network. The problem is formulated as dynamic multi- objective Markov decision processes (MOMDPs). MOM- DQL can explore the optimal eco-routes with respect to drivers’ different preferences on saving travel time and fuel. The MOM-DQL agent is trained under a series of learning environments that are based on historical vehicle trajectories, fuel consumption, and traffic signal status in the remote data center. The model that represents the action value function of the historical dynamic driving conditions can be downloaded to the vehicle requesting eco-routing service. The model can quickly adapt to the most recent driving condition through online one-shot learning and predict the optimal eco-routes for the subsequent unseen driving condition of the signalized traffic network. Simulation shows that the MOM-DQL method can discover the optimal eco-routes, and saves 52% travel time and 33% fuel, compared to the shortest-path strategy that is widely used in navigation systems.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Meta-Deep Q-Learning for Eco-Routing


    Beteiligte:
    Ma, Xin (Autor:in) / Xie, Yuanchang (Autor:in) / Chigan, Chunxiao (Autor:in)


    Erscheinungsdatum :

    2019-09-01


    Format / Umfang :

    198332 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Learning 2-Opt Heuristics for Routing Problems via Deep Reinforcement Learning

    de O. da Costa, Paulo R. / Rhuggenaath, Jason / Zhang, Yingqian et al. | BASE | 2021

    Freier Zugriff

    Deep Reinforcement Learning-Based Routing for Space-Terrestrial Networks

    Tsai, Kai-Chu / Yao, Ting-Jui / Huang, Pin-Hao et al. | IEEE | 2022


    Image-based Deep Reinforcement Meta-Learning for Autonomous Lunar Landing

    Andrea Scorsoglio / Andrea D’Ambrosio / Luca Ghilardi et al. | BASE | 2021

    Freier Zugriff

    Image-Based Deep Reinforcement Meta-Learning for Autonomous Lunar Landing

    Scorsoglio, Andrea / D’Ambrosio, Andrea / Ghilardi, Luca et al. | AIAA | 2021