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1
Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review
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Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review

Di Cao ; Weihao Hu ; Junbo Zhao ; Guozhou Zhang ; Bin Zhang ; Zhou Liu ; Zhe Chen ; Frede Blaabjerg

Journal of modern power systems and clean energy, 2020-11, Vol.8 (6), p.1029-1042 [Periódico revisado por pares]

IEEE

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2
How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries–A review and research agenda
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How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries–A review and research agenda

Singh, Vinay ; Chen, Shiuann-Shuoh ; Singhania, Minal ; Nanavati, Brijesh ; kar, Arpan kumar ; Gupta, Agam

International journal of information management data insights, 2022-11, Vol.2 (2), p.100094, Article 100094 [Periódico revisado por pares]

Elsevier

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3
Reinforcement Learning Models and Algorithms for Diabetes Management
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Reinforcement Learning Models and Algorithms for Diabetes Management

Yau, Kok-Lim Alvin ; Chong, Yung-Wey ; Fan, Xiumei ; Wu, Celimuge ; Saleem, Yasir ; Lim, Phei-Ching

IEEE access, 2023-01, Vol.11, p.1-1 [Periódico revisado por pares]

Piscataway: IEEE

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4
Molecular de-novo design through deep reinforcement learning
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Molecular de-novo design through deep reinforcement learning

Olivecrona, Marcus ; Blaschke, Thomas ; Engkvist, Ola ; Chen, Hongming

Journal of cheminformatics, 2017-09, Vol.9 (1), p.48-14, Article 48 [Periódico revisado por pares]

Cham: Springer International Publishing

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5
Hierarchical Reinforcement Learning: A Survey and Open Research Challenges
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Hierarchical Reinforcement Learning: A Survey and Open Research Challenges

Hutsebaut-Buysse, Matthias ; Mets, Kevin ; Latré, Steven

Machine learning and knowledge extraction, 2022-03, Vol.4 (1), p.172-221 [Periódico revisado por pares]

Basel: MDPI AG

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6
An adaptable and personalized framework for top-N course recommendations in online learning
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An adaptable and personalized framework for top-N course recommendations in online learning

Amin, Samina ; Uddin, M Irfan ; Alarood, Ala Abdulsalam ; Mashwani, Wali Khan ; Alzahrani, Ahmed Omar ; Alzahrani, Hamdan Ahmed

Scientific reports, 2024-05, Vol.14 (1), p.10382-10382, Article 10382 [Periódico revisado por pares]

England: Nature Publishing Group

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7
A review of reinforcement learning based hyper-heuristics
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A review of reinforcement learning based hyper-heuristics

Li, Cuixia ; Wei, Xiang ; Wang, Jing ; Wang, Shuozhe ; Zhang, Shuyan

PeerJ. Computer science, 2024-06, Vol.10, p.e2141, Article e2141 [Periódico revisado por pares]

PeerJ Inc

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8
A Robust Approach for Continuous Interactive Actor-Critic Algorithms
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A Robust Approach for Continuous Interactive Actor-Critic Algorithms

Millan-Arias, Cristian C. ; Fernandes, Bruno J. T. ; Cruz, Francisco ; Dazeley, Richard ; Fernandes, Sergio

IEEE access, 2021, Vol.9, p.104242-104260 [Periódico revisado por pares]

Piscataway: IEEE

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9
Joint Optimization of Multi-UAV Target Assignment and Path Planning Based on Multi-Agent Reinforcement Learning
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Joint Optimization of Multi-UAV Target Assignment and Path Planning Based on Multi-Agent Reinforcement Learning

Qie, Han ; Shi, Dianxi ; Shen, Tianlong ; Xu, Xinhai ; Li, Yuan ; Wang, Liujing

IEEE access, 2019, Vol.7, p.146264-146272 [Periódico revisado por pares]

Piscataway: IEEE

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10
A Survey of Multi-Task Deep Reinforcement Learning
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A Survey of Multi-Task Deep Reinforcement Learning

Vithayathil Varghese, Nelson ; Mahmoud, Qusay H.

Electronics (Basel), 2020-09, Vol.9 (9), p.1363 [Periódico revisado por pares]

Basel: MDPI AG

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