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

• URL: http://arxiv.org/abs/2110.05205v1
• Date: Mon, 11 Oct 2021 12:15:06 GMT
• Title: Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning
• Authors: Niranjan Deshpande (CHROMA), Dominique Vaufreydaz (M-PSI), Anne Spalanzani (CHROMA)
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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