This paper aims to understand how to guide travelers' routing behavior toward a system optimum, using a bilevel game in a multi-agent traffic environment. With the goal to optimize some systematic objective (e.g., overall traffic condition) of city planners, we formulate a Stackelberg game with the upper level as the planner and the lower level as a multi-agent Markov game in which each rational and selfish traveler aims to minimize her travel cost. We employ a Bayesian optimization method on the upper level to solve for optimal controls of city planners and a mean field multi-agent deep Q learning approach to solve for optimal route choices of travelers on the lower level. We demonstrate the effect of two administrative measures, namely tolling and signal control, on the behavior of travelers on the Braess network and a large-sized real-world road network, respectively. Braess paradox, which is usually defined in static user equilibrium, is also defined and discovered in the context of the multi-agent Markov game.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Bayesian Optimization for Multi-Agent Routing in Markov Games


    Contributors:
    Shou, Zhenyu (author) / Chen, Xu (author) / Di, Xuan (author)


    Publication date :

    2022-10-08


    Size :

    1283131 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Drone Delivery Multi-Agent Routing Optimization

    Van Gijseghem, Wouter / Agarwal, Umang | AIAA | 2020


    DRONE DELIVERY MULTI-AGENT ROUTING OPTIMIZATION

    Gijseghem, Wouter Van / Agarwal, Umang | TIBKAT | 2020



    Bayesian Optimization for Crowd Traffic Control Using Multi-Agent Simulation

    Otsuka, Takuma / Shimizu, Hitoshi / Iwata, Tomoharu et al. | IEEE | 2019