In this study, we elaborate the procedure of designing a supervisory controller for the Autonomous Transit on Demand Vehicle (ATODV) system. Reinforcement learning is implemented to reduce the mean waiting time of the passengers, and a cost function is introduced to penalize the energy consumption of the electric vehicles. A stochastic simulation environment for an ATODV pilot project is coded in the Python environment to train the autonomous cart decision process as agents with artificial intelligence. Passenger group behavior, get-on and get-off times, destinations are modeled as random variables. A single Deep Q-Learning Network is trained subject to multi-agent settings. The ATODV system's independent decision making for the carts to reduce the passenger's waiting time while constraining the energy consumption and empty vehicle motion is evaluated.


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    Titel :

    Multi-Agent Reinforcement Learning for Autonomous On Demand Vehicles


    Beteiligte:
    Boyali, Ali (Autor:in) / Hashimoto, Naohisa (Autor:in) / John, Vijay (Autor:in) / Acarman, Tankut (Autor:in)


    Erscheinungsdatum :

    01.06.2019


    Format / Umfang :

    555184 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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