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Transfer Learning in Deep Reinforcement Learning: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in reinforcement learning upon the fast development of deep neural networks. Along with the promising prospects of reinforcement learning in numerous domains such as robotics and game-playing, transfer learning ...
Zhuangdi Zhu +3 more
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Sensors, 2023 Manufacturing systems need to be resilient and self-organizing to adapt to unexpected disruptions, such as product changes or rapid order, in supply chain changes while increasing the automation level of robotized logistics processes to cope with the ...
Shokhikha Amalana Murdivien, Jumyung Um
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Improvement of Deep Reinforcement Learning Using Curriculum in Game Environment
مجله پژوهش در علوم توانبخشی, 2020 Introduction: Training deep curriculum learning is a kind of smart agent training in which, first the simple acts, and then, the difficult acts are trained to smart agent.
Mohammadreza Mohammadnejad +2 more
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Applied Sciences, 2022 In recent years, the recommendation system and robot learning are undoubtedly the two most popular application fields, and the core algorithms supporting these two fields are deep learning based on perception and reinforcement learning based on ...
Huaidong Yu, Jian Yin
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Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications
IEEE Access, 2020 Predicting crop yield based on the environmental, soil, water and crop parameters has been a potential research topic. Deep-learning-based models are broadly used to extract significant crop features for prediction. Though these methods could resolve the
Dhivya Elavarasan +1 more
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Deep Reinforcement Learning [PDF]
, 2019 Die Interaktion mit der umgebenden Welt kann als eine Grundlage des menschlichen Lernens betrachtet werden [93]. Kinder, welche die notigen motorischen Ablaufe zum Besteigen einer Treppe erlernen, haben dafur keinen direkten Lehrer. Stattdessen erarbeiten sie sich die entsprechenden Bewegungsablaufe durch Fehlschlage und Erfolge, durch das Studieren ...
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Exploration in deep reinforcement learning: A survey
Information Fusion, 2022 This paper reviews exploration techniques in deep reinforcement learning. Exploration techniques are of primary importance when solving sparse reward problems. In sparse reward problems, the reward is rare, which means that the agent will not find the reward often by acting randomly.
Pawel Ladosz +3 more
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Deep reinforcement learning for imbalanced classification [PDF]
Applied Intelligence, 2020 Data in real-world application often exhibit skewed class distribution which poses an intense challenge for machine learning. Conventional classification algorithms are not effective in the case of imbalanced data distribution, and may fail when the data distribution is highly imbalanced.
Qi Xiaoming, Chen Qiong, Enlu Lin
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PGN: A perturbation generation network against deep reinforcement learning [PDF]
arXiv, 2023 Deep reinforcement learning has advanced greatly and applied in many areas. In this paper, we explore the vulnerability of deep reinforcement learning by proposing a novel generative model for creating effective adversarial examples to attack the agent. Our proposed model can achieve both targeted attacks and untargeted attacks.
arxiv
Applied Sciences, 2023 The optimization of caching mechanisms has long been a crucial research focus in cloud–edge collaborative environments. Effective caching strategies can substantially enhance user experience quality in these settings.
Xinyu Zhang +6 more
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