Molecular optimization using a conditional transformer for reaction-aware compound exploration with reinforcement learning by Shogo Nakamura, Nobuaki Yasuo, Masakazu Sekijima

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The White Matter Functional Abnormalities in Patients with Transient Ischemic Attack: A Reinforcement Learning Approach by Huibin Ma, Zhou Xie, Lina Huang, Yanyan Gao, Linlin Zhan, Su Hu, Jiaxi Zhang, Qingguo Ding

“…Furthermore, a reinforcement learning method was used to investigate whether altered WM function could be a diagnostic indicator of TIA. …”
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Solving a Joint Pricing and Inventory Control Problem for Perishables via Deep Reinforcement Learning by Rui Wang, Xianghua Gan, Qing Li, Xiao Yan

“…We also show that our deep reinforcement learning algorithm achieves a better performance than tabular-based Q-learning algorithms. …”
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Reactive power optimization via deep transfer reinforcement learning for efficient adaptation to multiple scenarios by Congbo Bi, Di Liu, Lipeng Zhu, Chao Lu, Shiyang Li, Yingqi Tang

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Bi-Level Game Strategy for Virtual Power Plants Based on an Improved Reinforcement Learning Algorithm by Zhu Liu, Guowei Guo, Dehuang Gong, Lingfeng Xuan, Feiwu He, Xinglin Wan, Dongguo Zhou

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Maximizing network throughput by cooperative reinforcement learning in clustered solar-powered wireless sensor networks by Yujia Ge, Yurong Nan, Xianhai Guo

“…This article proposes an adaptive power manager based on cooperative reinforcement learning methods for the solar-powered wireless sensor networks to keep harvested energy more balanced among the whole clustered network. …”
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Deep Reinforcement Learning-Based Resource Allocation for QoE Enhancement in Wireless VR Communications by Georgios Kougioumtzidis, Vladimir K. Poulkov, Pavlos I. Lazaridis, Zaharias D. Zaharis

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Efficient Handling of Data Imbalance in Health Insurance Fraud Detection Using Meta-Reinforcement Learning by Supriya Seshagiri, K. V. Prema

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Learning the Car-following Behavior of Drivers Using Maximum Entropy Deep Inverse Reinforcement Learning by Yang Zhou, Rui Fu, Chang Wang

“…The learned rewards and strategies were consistent with the demonstrations of the two groups. Inverse reinforcement learning can serve as a new tool to explain and model driving behavior, providing references for the development of human-like autonomous driving models.…”
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Adaptive Coordinated Variable Speed Limit between Highway Mainline and On-Ramp with Deep Reinforcement Learning by Ming Cheng, Chenghao Zhang, Hui Jin, Ziming Wang, Xiaoguang Yang

“…The method of deep reinforcement learning based on deep deterministic policy gradient is employed to solve the proposed model with a row of continuous control variables. …”
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Research on Immersion Teaching Method Based on 5G +XR Technology and Reinforcement Learning Model by Xueqing Xie

“…In addition, based on the analysis of XR teaching theory and its significance, an immersive XR teaching design method based on reinforcement learning model was proposed, and the working principle of immersive XR teaching system and the architecture method of teaching space were given. …”
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Optimal Skipping Rates: Training Agents with Fine-Grained Control Using Deep Reinforcement Learning by Adil Khan, Jiang Feng, Shaohui Liu, Muhammad Zubair Asghar

“…Similarly, ViZDoom is a game artificial intelligence research platform based on Doom used for visual deep reinforcement learning in 3D game environments such as first-person shooters (FPS). …”
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Towards an optimization of catheter guidance in vascular surgery: A comparative analysis of the contribution of reinforcement learning by Cheima Bouden

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Low-Shot Wall Defect Detection for Autonomous Decoration Robots Using Deep Reinforcement Learning by Fanyu Zeng, Xi Cai, Shuzhi Sam Ge

“…Finally, we train our method with deep reinforcement learning to learn the optimal detection policy. …”
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Reinforcement Learning-Based Routing Protocol to Minimize Channel Switching and Interference for Cognitive Radio Networks by Tauqeer Safdar Malik, Mohd Hilmi Hasan

“…The channel selection is performed on the basis of reinforcement learning algorithms such as No-External Regret Learning, Q-Learning, and Learning Automata. …”
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Influence maximization under imbalanced heterogeneous networks via lightweight reinforcement learning with prior knowledge by Kehong You, Sanyang Liu, Yiguang Bai

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Distributed secondary control for DC microgrids using two-stage multi-agent reinforcement learning by Fei Li, Weifei Tu, Yun Zhou, Heng Li, Feng Zhou, Weirong Liu, Chao Hu

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Hybrid intelligent optimization strategy of battery swapping station for electric vehicles based on reinforcement learning by Hongwei Yi, Huan Zhang, Jianghong Li, Yanling Zhao

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Explainable post hoc portfolio management financial policy of a Deep Reinforcement Learning agent by Alejandra de-la-Rica-Escudero, Eduardo C. Garrido-Merchán, María Coronado-Vaca

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Resource Allocation in D2D-Enabled 5G Networks Using Multiagent Reinforcement Learning by Kwame Opuni-Boachie Obour Agyekum, Alex Yaw Boakye, Benedict Appati, Jochebed Akoto Opoku, Justice Owusu Agyemang, Gordon Owusu Boateng, James Dzisi Gadze

“…This paper presents a novel approach using multiagent reinforcement learning with a proximal policy optimization algorithm to address the resource allocation problem in D2D networks. …”
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