Results 21 to 30 of about 57,740 (314)
Deep Reinforcement Learning for Cyber Security [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2023 The scale of Internet-connected systems has increased considerably, and these systems are being exposed to cyber attacks more than ever. The complexity and dynamics of cyber attacks require protecting mechanisms to be responsive, adaptive, and scalable. Machine learning, or more specifically deep reinforcement learning (DRL), methods have been proposed
Thanh Thi Nguyen, Vijay Janapa Reddi
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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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Real-time security margin control using deep reinforcement learning
Energy and AI, 2023 This paper develops a real-time control method based on deep reinforcement learning aimed to determine the optimal control actions to maintain a sufficient secure operating limit.
Hannes Hagmar +2 more
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Overview of Multi-agent Deep Reinforcement Learning Based on Value Factorization [PDF]
Jisuanji kexue, 2022 Multi-agent deep reinforcement learning based on value factorization is one of many multi-agent deep reinforcement learning algorithms,and it is also a research hotspot in the field of multi-agent deep reinforcement learning.Under some constraints,the ...
XIONG Li-qin, CAO Lei, LAI Jun, CHEN Xi-liang
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Deep Reinforcement Learning for Multiobjective Optimization [PDF]
IEEE Transactions on Cybernetics, 2021 This study proposes an end-to-end framework for solving multi-objective optimization problems (MOPs) using Deep Reinforcement Learning (DRL), that we call DRL-MOA. The idea of decomposition is adopted to decompose the MOP into a set of scalar optimization subproblems. Then each subproblem is modelled as a neural network.
Kaiwen Li, Tao Zhang, Rui Wang
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Deep Reinforcement Learning in Medicine [PDF]
Kidney Diseases, 2018 Reinforcement learning has achieved tremendous success in recent years, notably in complex games such as Atari, Go, and chess. In large part, this success has been made possible by powerful function approximation methods in the form of deep neural networks.
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Deep Reinforcement Learning with Interactive Feedback in a Human–Robot Environment
Applied Sciences, 2020 Robots are extending their presence in domestic environments every day, it being more common to see them carrying out tasks in home scenarios. In the future, robots are expected to increasingly perform more complex tasks and, therefore, be able to ...
Ithan Moreira +5 more
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Learning an Efficient Text Augmentation Strategy: A Case Study in Sentiment Analysis [PDF]
International Journal of Web Research, 2023 Contemporary machine learning models, like deep neural networks, require substantial labeled datasets for proper training. However, in areas such as natural language processing, a shortage of labeled data can lead to overfitting.
Mehdy Roayaei
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