Applications of Deep Reinforcement Learning in Communications and Networking: A Survey [PDF]
IEEE Communications Surveys and Tutorials, 2018 This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking. Modern networks, e.g., Internet of Things (IoT) and unmanned aerial vehicle (UAV) networks, become more ...
Nguyen Cong Luong +6 more
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Multi-agent deep reinforcement learning: a survey
Artificial Intelligence Review, 2021 The advances in reinforcement learning have recorded sublime success in various domains. Although the multi-agent domain has been overshadowed by its single-agent counterpart during this progress, multi-agent reinforcement learning gains rapid traction ...
Sven Gronauer, K. Diepold
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Self-Communicating Deep Reinforcement Learning Agents Develop External Number Representations [PDF]
, 2022 Symbolic numbers are a remarkable product ofhuman cultural development. The developmentalprocess involved the creation and progressive re-finement of material representational tools, suchas notched tallies, knotted strings, and countingboards.
Petruzzellis, Flavio +3 more
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Tongxin xuebao, 2023 To solve the problem that intelligent devices equipped with deep reinforcement learning agents lack effective security data sharing mechanisms in the intelligent Internet of things, a general federated reinforcement learning (GenFedRL) framework was ...
Biao JIN +4 more
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Deep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Review
Robotics, 2021 Deep learning has provided new ways of manipulating, processing and analyzing data. It sometimes may achieve results comparable to, or surpassing human expert performance, and has become a source of inspiration in the era of artificial intelligence ...
Rongrong Liu +4 more
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Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations [PDF]
Robotics: Science and Systems, 2017 Dexterous multi-fingered hands are extremely versatile and provide a generic way to perform multiple tasks in human-centric environments. However, effectively controlling them remains challenging due to their high dimensionality and large number of ...
A. Rajeswaran +5 more
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Steiner tree: a deep reinforcement learning approach
, 2021 Tong, GuangmoThe Steiner tree problem is a classical combinatorial optimization problem that targets interconnecting a set of points by a network whose total length is the shortest, where the network consists of the original points and the newly added ...
Wang, Siqi
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Off-policy Maximum Entropy Deep Reinforcement Learning Algorithm Based on RandomlyWeighted Triple Q -Learning [PDF]
Jisuanji kexue, 2022 Reinforcement learning is an important branch of machine learning.With the development of deep learning,deep reinforcement learning research has gradually developed into the focus of reinforcement learning research.Model-free off-policy deep ...
FAN Jing-yu, LIU Quan
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Target‐driven visual navigation in indoor scenes using reinforcement learning and imitation learning
CAAI Transactions on Intelligence Technology, 2022 Here, the challenges of sample efficiency and navigation performance in deep reinforcement learning for visual navigation are focused and a deep imitation reinforcement learning approach is proposed.
Qiang Fang +3 more
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A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation
Sensors, 2023 Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems.
Dong Han +3 more
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