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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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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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Agronomy, 2022 A series-parallel hybrid banana-harvesting robot was previously developed to pick bananas, with inverse kinematics intractable to an address. This paper investigates a deep reinforcement learning-based inverse kinematics solution to guide the banana ...
Guichao Lin +5 more
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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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Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey
Sensors, 2023 Deep reinforcement learning has produced many success stories in recent years. Some example fields in which these successes have taken place include mathematics, games, health care, and robotics. In this paper, we are especially interested in multi-agent
James Orr, Ayan Dutta
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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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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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Deep Reinforcement Learning based Patch Selection for Illuminant Estimation [PDF]
, 2019 Previous deep learning based approaches to illuminant estimation either resized the raw image to lower resolution or randomly cropped image patches for the deep learning model.
Hou, Xianxu +4 more
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Scenario-assisted Deep Reinforcement Learning [PDF]
Proceedings of the 10th International Conference on Model-Driven Engineering and Software Development, 2022 Deep reinforcement learning has proven remarkably useful in training agents from unstructured data. However, the opacity of the produced agents makes it difficult to ensure that they adhere to various requirements posed by human engineers. In this work-in-progress report, we propose a technique for enhancing the reinforcement learning training process (
Yerushalmi, Raz +5 more
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Routing algorithms as tools for integrating social distancing with emergency evacuation
Scientific Reports, 2021 One of the lessons from the COVID-19 pandemic is the importance of social distancing, even in challenging circumstances such as pre-hurricane evacuation. To explore the implications of integrating social distancing with evacuation operations, we describe
Yi-Lin Tsai +3 more
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