Virtual crowd simulation is being used in a wide variety of applications such as video games, architectural designs and movies. It is important for creators to have a realistic crowd simulator that will be able to generate crowds that displays the behaviours needed. It is important to provide an easy to use tool for crowd generation which is fast and realistic. Reinforcement Learning was proposed for training an agent to display a certain behaviour. In this thesis, a Reinforcement Learning approach was implemented and the generated virtual crowds were evaluated. Q Learning method was selected as the Reinforcement Learning method. Two different versions of the Q Learning method was implemented. These different versions were evaluated with respect to state-of-the-art algorithms: Reciprocal Velocity Obstacles(RVO) and a copy-synthesis approach based on real-data. Evaluation of the crowds was done with a user study. Results from the user study showed that while Reinforcement Learning method is not perceived as real as the real crowds, it was perceived almost as realistic as the crowds generated with RVO. Another result was that, the perception of RVO changes with the changing environment. When only the paths were shown, RVO was perceived as being more natural than when the paths were shown in a setting in real world with pedestrians. It was concluded that using Q Learning for generating virtual crowds is a promising method and can be improved as a substitute for existing methods and in certain scenarios, Q Learning algorithm results with better collision avoidance and more realistic crowd simulation. ; Virtuell folkmassimulering används i ett brett utbud av applikationersom videospel, arkitektoniska mönster och filmer. Det är viktigt förskaparna att ha en realistisk publik simulator som kommer att kunnagenerera publiken som behövs för att visa de beteenden som behövs. Det är viktigt att tillhandahålla ett lättanvänt verktyg för publikgenereringsom är snabb och realistisk. Förstärkt lärande föreslogs föratt utbilda ...


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    Titel :

    Collision Avoidance for Virtual Crowds Using Reinforcement Learning ; Kollisionsundvikande för virtuella folkmassor som använder förstärkningslärande


    Beteiligte:

    Erscheinungsdatum :

    2017-01-01


    Medientyp :

    Hochschulschrift


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629




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