RMGANets: reinforcement learning-enhanced multi-relational attention graph-aware network for anti-money laundering detection by Qianyu Wang, Wei-Tek Tsai, Bowen Du

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Blockchain-Powered Secure and Scalable Threat Intelligence System With Graph Convolutional Autoencoder and Reinforcement Learning Feedback Loop by Mohamad Khayat, Ezedin Barka, Mohamed Adel Serhani, Farag Sallabi, Khaled Shuaib, Heba M. Khater

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Data Driven Real-Time Dynamic Voltage Control Using Decentralized Execution Multi-Agent Deep Reinforcement Learning by Yuling Wang, Vijay Vittal

“…To enhance the controller’s resilience in addressing communication failures, a dynamic voltage control method employing distributed execution multi-agent deep reinforcement learning(DRL) is proposed. The proposed method follows a centralized training and decentralized execution based approach. …”
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On Explainability of Reinforcement Learning-Based Machine Learning Agents Trained with Proximal Policy Optimization That Utilizes Visual Sensor Data by Tomasz Hachaj, Marcin Piekarczyk

Subjects: “…reinforcement learning…”
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A proximal policy optimization based deep reinforcement learning framework for tracking control of a flexible robotic manipulator by Joshi Kumar V, Vinodh Kumar Elumalai

Subjects: “…Deep reinforcement learning…”
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Load frequency control in isolated island city microgrids using deep graph reinforcement learning considering extensive scenarios by Ping He, Xiongwei Huang, Ruobing He, Linkun Yuan

“…By incorporating a maximum entropy approach, it enhances the robustness of conventional deep reinforcement learning and integrates dominance learning to refine Soft Actor-Critic’s Q-value function update, mitigating overestimation issues and boosting algorithmic performance. …”
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Enhancing Mixed Traffic Flow Safety via Connected and Autonomous Vehicle Trajectory Planning with a Reinforcement Learning Approach by Yanqiu Cheng, Chenxi Chen, Xianbiao Hu, Kuanmin Chen, Qing Tang, Yang Song

“…This study presents a reinforcement learning modeling approach, named Monte Carlo tree search-based autonomous vehicle safety algorithm, or MCTS-AVS, to optimize the safety of mixed traffic flow, on a one-lane roadway with signalized intersection control. …”
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3D LiDAR Multi-Object Tracking Using Multi Positive Contrastive Learning and Deep Reinforcement Learning by Minho Cho, Euntai Kim

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Trajectory Optimization of CAVs in Freeway Work Zone considering Car-Following Behaviors Using Online Multiagent Reinforcement Learning by Tong Zhu, Xiaohu Li, Wei Fan, Changshuai Wang, Haoxue Liu, Runqing Zhao

“…The multiagent reinforcement learning (MARL) method is applied in this system, with one agent providing a merging advisory service at the merging point and controlling the inner-lane vehicles’ headway for smooth outer-lane vehicle merging, while other agents provide lane-changing advisory services at advance lane-changing points to control how vehicles make lane changes in advance and perform corresponding headway adjustment, similar to and jointly with the merging advisory service. …”
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A generic self-learning emotional framework for machines by Alberto Hernández-Marcos, Eduardo Ros

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Steganography in Game Actions by Ching-Chun Chang, Isao Echizen

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Bayesian reinforcement learning models reveal how great-tailed grackles improve their behavioral flexibility in serial reversal learning experiments by Lukas, Dieter, McCune, Kelsey, Blaisdell, Aaron, Johnson-Ulrich, Zoe, MacPherson, Maggie, Seitz, Benjamin, Sevchik, August, Logan, Corina

“…Here, we apply and expand newly developed Bayesian reinforcement learning models to gain additional insights into how individuals might dynamically modulate their behavioral flexibility if they experience serial reversals. …”
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Two-stage multi-timescale optimal scheduling for electricity-hydrogen coupling systems based on scenario approach and deep reinforcement learning by CHEN Zhe, WEI Meijia, LIN Da, LI Zhihao, CHEN Jian

“…To address these challenges， this paper， using scenario approach and deep reinforcement learning （DRL）， proposes a two-stage multi-timescale optimal scheduling method for electricity-hydrogen coupling systems considering uncertainties of wind and solar power generation. …”
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Crowding distance and IGD-driven grey wolf reinforcement learning approach for multi-objective agile earth observation satellite scheduling by He Wang, Weiquan Huang, Sindri Magnússon, Tony Lindgren, Chen Chen, Junyu Wu, Yanjie Song

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Assessment of Rear-End Collision Risk Based on a Deep Reinforcement Learning Technique: A Break Reaction Assessment Approach by Muhammad Sameer Sheikh, Yinqiao Peng

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The joint policy of production, maintenance, and product quality in a multi-machine production system by reinforcement learning and agent-based modeling by Mohammad Reza Nazabadi, Seyed Najafi, Ali Mohaghar, Farzad Movahedi Sobhani

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A multimodal dataset for robotic peg extraction based on Bioin-Tacto sensor modulesMendeley Data by Viral Galayia, Ruslan Masinjila, Soheil Khatibi, Thiago Eustaquio Alves de Oliveira, Xianta Jiang, Vinicius Prado da Fonseca

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Q-learning global path planning for UAV navigation with pondered priorities by Kevin B. de Carvalho, Hiago de O.B. Batista, Leonardo A. Fagundes-Junior, Iure Rosa L. de Oliveira, Alexandre S. Brandão

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Learning-based locomotion control fusing multimodal perception for a bipedal humanoid robot by Chao Ji, Diyuan Liu, Wei Gao, Shiwu Zhang

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Negative valuation of ambiguous feedback may predict near-term risk for suicide attempt in Veterans at high risk for suicide by Catherine E. Myers, Catherine E. Myers, Rokas Perskaudas, Rokas Perskaudas, Vibha Reddy, Chintan V. Dave, Chintan V. Dave, John G. Keilp, Arlene King, Kailyn Rodriguez, Kailyn Rodriguez, Lauren St. Hill, Rachael Miller, Alejandro Interian, Alejandro Interian

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