Efficient deep reinforcement learning with imitative expert priors for autonomous driving

Z Huang, J Wu, C Lv - IEEE Transactions on Neural Networks …, 2022 - ieeexplore.ieee.org
Deep reinforcement learning (DRL) is a promising way to achieve human-like autonomous
driving. However, the low sample efficiency and difficulty of designing reward functions for …
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Efficient Deep Reinforcement Learning With Imitative Expert Priors for Autonomous Driving.

Z Huang, J Wu, C Lv - IEEE Transactions on Neural Networks and …, 2022 - europepmc.org
Deep reinforcement learning (DRL) is a promising way to achieve human-like autonomous
driving. However, the low sample efficiency and difficulty of designing reward functions for …

Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Driving

Z Huang, J Wu, C Lv - arXiv preprint arXiv:2103.10690, 2021 - arxiv.org
Deep reinforcement learning (DRL) is a promising way to achieve human-like autonomous
driving. However, the low sample efficiency and difficulty of designing reward functions for …

Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Driving

Z Huang, J Wu, C Lv - arXiv e-prints, 2021 - ui.adsabs.harvard.edu
Deep reinforcement learning (DRL) is a promising way to achieve human-like autonomous
driving. However, the low sample efficiency and difficulty of designing reward functions for …

Efficient Deep Reinforcement Learning With Imitative Expert Priors for Autonomous Driving

Z Huang, J Wu, C Lv - IEEE transactions on neural …, 2023 - pubmed.ncbi.nlm.nih.gov
Deep reinforcement learning (DRL) is a promising way to achieve human-like autonomous
driving. However, the low sample efficiency and difficulty of designing reward functions for …