A survey of roughly 100 CARLA reinforcement learning papers, mapping algorithm families, representations, rewards, evaluation metrics, towns, and open challenges.
Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning
1 Pith paper cite this work, alongside 10 external citations. Polarity classification is still indexing.
1
Pith paper citing it
10
external citations · Pith
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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cs.RO 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator
A survey of roughly 100 CARLA reinforcement learning papers, mapping algorithm families, representations, rewards, evaluation metrics, towns, and open challenges.