[PDF] arxiv.org

Semisupervised deep reinforcement learning in support of IoT and smart city services

M Mohammadi, A Al-Fuqaha… - IEEE Internet of Things …, 2017 - ieeexplore.ieee.org
Smart services are an important element of the smart cities and the Internet of Things (IoT)
ecosystems where the intelligence behind the services is obtained and improved through …
被引用次数：464 相关文章 所有 6 个版本
[PDF] arxiv.org

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 …
被引用次数：101 相关文章 所有 5 个版本
[PDF] arxiv.org

Applications of deep reinforcement learning in communications and networking: A survey

NC Luong, DT Hoang, S Gong, D Niyato… - … surveys & tutorials, 2019 - ieeexplore.ieee.org
This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
被引用次数：1686 相关文章 所有 7 个版本

[图书][B] Deep Reinforcement Learning

H Dong, H Dong, Z Ding, S Zhang, Chang - 2020 - Springer
Deep reinforcement learning (DRL) combines deep learning (DL) with a reinforcement
learning (RL) architecture. It has been able to perform a wide range of complex decision …
被引用次数：212 相关文章 所有 4 个版本
[PDF] arxiv.org

Deep reinforcement learning (DRL): Another perspective for unsupervised wireless localization

Y Li, X Hu, Y Zhuang, Z Gao, P Zhang… - ieee internet of things …, 2019 - ieeexplore.ieee.org
Location is key to spatialize Internet of Things (IoT) data. However, it is challenging to use
low-cost IoT devices for robust unsupervised localization (ie, localization without training …
被引用次数：108 相关文章 所有 5 个版本

Deep reinforcement learning on autonomous driving policy with auxiliary critic network

Y Wu, S Liao, X Liu, Z Li, R Lu - IEEE transactions on neural …, 2021 - ieeexplore.ieee.org
Deep reinforcement learning (DRL) is a machine learning method based on rewards, which
can be extended to solve some complex and realistic decision-making problems …
被引用次数：38 相关文章 所有 5 个版本

Deep reinforcement learning

M Krichen - 2023 14th International Conference on Computing …, 2023 - ieeexplore.ieee.org
Deep Reinforcement Learning (DRL) is a powerful technique for learning policies for
complex decision-making tasks. In this paper, we provide an overview of DRL, including its …
被引用次数：13 相关文章
[PDF] arxiv.org

Exploring applications of deep reinforcement learning for real-world autonomous driving systems

V Talpaert, I Sobh, BR Kiran, P Mannion… - arXiv preprint arXiv …, 2019 - arxiv.org
Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years,
with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed …
被引用次数：84 相关文章 所有 6 个版本
[PDF] google.com

Deep reinforcement learning for mobile 5G and beyond: Fundamentals, applications, and challenges

Z Xiong, Y Zhang, D Niyato, R Deng… - IEEE Vehicular …, 2019 - ieeexplore.ieee.org
Future-generation wireless networks (5G and beyond) must accommodate surging growth in
mobile data traffic and support an increasingly high density of mobile users involving a …
被引用次数：271 相关文章 所有 5 个版本
[PDF] arxiv.org

Human-in-the-loop deep reinforcement learning with application to autonomous driving

J Wu, Z Huang, C Huang, Z Hu, P Hang, Y Xing… - arXiv preprint arXiv …, 2021 - arxiv.org
Due to the limited smartness and abilities of machine intelligence, currently autonomous
vehicles are still unable to handle all kinds of situations and completely replace drivers …
被引用次数：43 相关文章 所有 3 个版本