Maneuvering in dense traffic is a challenging task for autonomous vehicles because it requires reasoning about the stochastic behaviors of many other participants. In addition, the agent must achieve the maneuver within a limited time and distance. In this work, we propose a combination of reinforcement learning and game theory to learn merging behaviors. We design a training curriculum for a reinforcement learning agent using the concept of level-k behavior. This approach exposes the agent to a broad variety of behaviors during training, which promotes learning policies that are robust to model discrepancies. We show that our approach learns more efficient policies than traditional training methods.


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    Title :

    Reinforcement Learning with Iterative Reasoning for Merging in Dense Traffic


    Contributors:


    Publication date :

    2020-09-20


    Size :

    895571 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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