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Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning

Niranjan Deshpande 1 Dominique Vaufreydaz 2 Anne Spalanzani 1 
1 CHROMA - Robots coopératifs et adaptés à la présence humaine en environnements dynamiques
Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services
2 M-PSI - Multimodal Perception and Sociable Interaction
LIG - Laboratoire d'Informatique de Grenoble
Abstract : 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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https://hal.inria.fr/hal-03372856
Contributor : Dominique Vaufreydaz Connect in order to contact the contributor
Submitted on : Monday, October 11, 2021 - 10:48:54 AM
Last modification on : Wednesday, July 6, 2022 - 4:23:11 AM
Long-term archiving on: : Wednesday, January 12, 2022 - 7:13:51 PM

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Niranjan Deshpande, Dominique Vaufreydaz, Anne Spalanzani. Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning. ITSC 2021 - 24th IEEE International Conference on Intelligent Transportation Systems, Sep 2021, Indianapolis, United States. pp.1-7, ⟨10.1109/ITSC48978.2021.9564601⟩. ⟨hal-03372856⟩

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