Urban autonomous driving in the presence of pedestrians as vulnerable road users is still a challenging and less examined research problem. This work formulates navigation in urban environments as a multi objective reinforcement learning problem. A deep learning variant of thresholded lexicographic Q-learning is presented for autonomous navigation amongst pedestrians. The multi objective DQN agent is trained on a custom urban environment developed in CARLA simulator. The proposed method is evaluated by comparing it with a single objective DQN variant on known and unknown environments. Evaluation results show that the proposed method outperforms the single objective DQN variant with respect to all aspects.


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    Title :

    Navigation in Urban Environments amongst pedestrians using Multi-Objective Deep Reinforcement Learning


    Contributors:


    Publication date :

    2021-09-19


    Size :

    344144 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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