GenFedRL: a general federated reinforcement learning framework for deep reinforcement learning agents by Biao JIN, Yikang LI, Zhiqiang YAO, Yulin CHEN, Jinbo XIONG

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Neuroevolutionary reinforcing learning of neural networks by Y. A. Bury, D. I. Samal

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Reinforcement learning for deep portfolio optimization by Ruyu Yan, Jiafei Jin, Kun Han

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Reinforcement Learning for Reconfigurable Robotic Soccer by M. Shameer Ahamed, J. J. J. Pey, S. M. Bhagya P. Samarakoon, M. A. Viraj J. Muthugala, Mohan Rajesh Elara

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Reinforcement Learning Based Artificial Immune Classifier by Mehmet Karakose

“…This approach uses reinforcement learning to find better antibody with immune operators. …”
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Mix-Spectrum for Generalization in Visual Reinforcement Learning by Jeong Woon Lee, Hyoseok Hwang

Subjects: “…Deep reinforcement learning…”
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Deep Reinforcement Learning for Intraday Multireservoir Hydropower Management by Rodrigo Castro-Freibott, Álvaro García-Sánchez, Francisco Espiga-Fernández, Guillermo González-Santander de la Cruz

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Research on the Application of Reinforcement Learning in Traffic Flow Prediction by Hu Yiquan

“…The purpose of this paper is to discuss how Reinforcement Learning (RL) can be applied to TFP. RL optimizes strategies through interactions between agents and the environment to maximize cumulative rewards. …”
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Online hierarchical reinforcement learning based on interrupting Option by Fei ZHU, Zhi-peng XU, Quan LIU, Yu-chen FU, Hui WANG

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Intelligent Inventory Control via Ruminative Reinforcement Learning by Tatpong Katanyukul, Edwin K. P. Chong

“…Ruminative reinforcement learning (RRL) has been introduced recently based on this approach. …”
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Service chain mapping algorithm based on reinforcement learning by Liang WEI, Tao HUANG, Jiao ZHANG, Zenan WANG, Jiang LIU, Yunjie LIU

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Memory consolidation from a reinforcement learning perspective by Jong Won Lee, Min Whan Jung, Min Whan Jung

“…This framework closely parallels Dyna, a reinforcement learning algorithm introduced by Sutton in 1991. …”
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Hamilton-Jacobi Reachability in Reinforcement Learning: A Survey by Milan Ganai, Sicun Gao, Sylvia L. Herbert

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Physics-guided actor-critic reinforcement learning for swimming in turbulence by Christopher Koh, Laurent Pagnier, Michael Chertkov

“…We explore optimally balancing these efforts by developing a novel physics-informed reinforcement learning strategy and comparing it with prescribed control and physics-agnostic reinforcement learning strategies. …”
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Music Emotion Research Based on Reinforcement Learning and Multimodal Information by Yue Hu

“…In this paper, a multimodal fusion algorithm for music emotion analysis is proposed, and a dynamic model based on reinforcement learning is constructed to improve the analysis accuracy. …”
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Drone Landing and Reinforcement Learning: State-of-Art, Challenges and Opportunities by Jose Amendola, Linga Reddy Cenkeramaddi, Ajit Jha

Subjects: “…Deep reinforcement learning…”
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Application of Reinforcement Learning in Cognitive Radio Networks: Models and Algorithms by Kok-Lim Alvin Yau, Geong-Sen Poh, Su Fong Chien, Hasan A. A. Al-Rawi

“…Cognitive radio (CR) enables unlicensed users to exploit the underutilized spectrum in licensed spectrum whilst minimizing interference to licensed users. Reinforcement learning (RL), which is an artificial intelligence approach, has been applied to enable each unlicensed user to observe and carry out optimal actions for performance enhancement in a wide range of schemes in CR, such as dynamic channel selection and channel sensing. …”
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Autonomous Bus Fleet Control Using Multiagent Reinforcement Learning by Sung-Jung Wang, S. K. Jason Chang

“…The experimental results indicate that the developed algorithm outperforms other reinforcement learning methods in the multi-agent domain. …”
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Enhancing cotton irrigation with distributional actor–critic reinforcement learning by Yi Chen, Meiwei Lin, Zhuo Yu, Weihong Sun, Weiguo Fu, Liang He

Subjects: “…Distributional reinforcement learning…”
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RL4CEP: reinforcement learning for updating CEP rules by Afef Mdhaffar, Ghassen Baklouti, Yassine Rebai, Mohamed Jmaiel, Bernd Freisleben

Subjects: “…Deep Reinforcement Learning…”
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