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arXiv · 2110.05205

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

Niranjan Deshpande·Dominique Vaufreydaz·Anne Spalanzani
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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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BibTeXRIS

Niranjan Deshpande, Dominique Vaufreydaz, Anne Spalanzani. 2021-10-11. Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning. https://arxiv.org/abs/2110.05205

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