SmartGov : architecture générique pour la co-construction de politiques urbaines basée sur l'apprentissage par renforcement multi-agent

Simon Pageaud 1, 2
2 SMA - Systèmes Multi-Agents
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : In this thesis, we propose the SmartGov model, coupling multi-agent simulation and multi-agent deep reinforcement learning, to help co-construct urban policies and integrate all stakeholders in the decision process. Smart Cities provide sensor data from the urban areas to increase realism of the simulation in SmartGov.Our first contribution is a generic architecture for multi-agent simulation of the city to study global behavior emergence with realistic agents reacting to political decisions. With a multi-level modeling and a coupling of different dynamics, our tool learns environment specificities and suggests relevant policies. Our second contribution improves autonomy and adaptation of the decision function with multi-agent, multi-level reinforcement learning. A set of clustered agents is distributed over the studied area to learn local specificities without any prior knowledge on the environment. Trust score assignment and individual rewards help reduce non-stationary impact on experience replay in deep reinforcement learning.These contributions bring forth a complete system to co-construct urban policies in the Smart City. We compare our model with different approaches from the literature on a parking fee policy to display the benefits and limits of our contributions
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Simon Pageaud. SmartGov : architecture générique pour la co-construction de politiques urbaines basée sur l'apprentissage par renforcement multi-agent. Intelligence artificielle [cs.AI]. Université de Lyon, 2019. Français. ⟨NNT : 2019LYSE1128⟩. ⟨tel-02475605⟩

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