| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Xingche Guo, Donglin Zeng, Yuanjia Wan. A Semiparametric Inverse Reinforcement Learning Approach to Characterize Decision Making for Mental Disorders. Journal of the American Statistical Association. vol 119. issue 545. 2024-05-06. PMID:38706706. |
motivated by the probabilistic reward task (prt) experiment in the embarc study, we propose a semiparametric inverse reinforcement learning (rl) approach to characterize the reward-based decision-making of mdd patients. |
2024-05-06 |
2024-05-08 |
human |
| Mokhaled N A Al-Hamadani, Mohammed A Fadhel, Laith Alzubaidi, Harangi Balaz. Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review. Sensors (Basel, Switzerland). vol 24. issue 8. 2024-04-27. PMID:38676080. |
reinforcement learning (rl) has emerged as a dynamic and transformative paradigm in artificial intelligence, offering the promise of intelligent decision-making in complex and dynamic environments. |
2024-04-27 |
2024-04-29 |
Not clear |
| Mokhaled N A Al-Hamadani, Mohammed A Fadhel, Laith Alzubaidi, Harangi Balaz. Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review. Sensors (Basel, Switzerland). vol 24. issue 8. 2024-04-27. PMID:38676080. |
this unique feature enables rl to address sequential decision-making problems with simultaneous sampling, evaluation, and feedback. |
2024-04-27 |
2024-04-29 |
Not clear |
| Muthulakshmi Karuppiyan, Hariharan Subramani, Shanthy Kandasamy Raju, Manimekalai Maradi Anthonymuthu Prakasa. Dynamic resource allocation in 5G networks using hybrid RL-CNN model for optimized latency and quality of service. Network (Bristol, England). 2024-04-10. PMID:38594948. |
by merging convolutional neural networks (cnn) for feature extraction and reinforcement learning (rl) for decision-making, drarlcnn optimizes resource allocation, minimizing latency and maximizing quality of service (qos). |
2024-04-10 |
2024-04-12 |
Not clear |
| Luca F Roggeveen, Ali El Hassouni, Harm-Jan de Grooth, Armand R J Girbes, Mark Hoogendoorn, Paul W G Elber. Reinforcement learning for intensive care medicine: actionable clinical insights from novel approaches to reward shaping and off-policy model evaluation. Intensive care medicine experimental. vol 12. issue 1. 2024-03-25. PMID:38526681. |
reinforcement learning (rl) holds great promise for intensive care medicine given the abundant availability of data and frequent sequential decision-making. |
2024-03-25 |
2024-03-28 |
Not clear |
| JiLe DeGe, Sina San. Optimization of news dissemination push mode by intelligent edge computing technology for deep learning. Scientific reports. vol 14. issue 1. 2024-03-21. PMID:38509163. |
compared with deep learning, rl is more suitable for scenes that need long-term decision-making and trial-and-error learning. |
2024-03-21 |
2024-03-23 |
Not clear |
| Zhiyue Zhang, Hongyuan Mei, Yanxun X. Continuous-Time Decision Transformer for Healthcare Applications. Proceedings of machine learning research. vol 206. 2024-03-04. PMID:38435084. |
offline reinforcement learning (rl) is a promising approach for training intelligent medical agents to learn treatment policies and assist decision making in many healthcare applications, such as scheduling clinical visits and assigning dosages for patients with chronic conditions. |
2024-03-04 |
2024-03-06 |
Not clear |
| Zhiyue Zhang, Hongyuan Mei, Yanxun X. Continuous-Time Decision Transformer for Healthcare Applications. Proceedings of machine learning research. vol 206. 2024-03-04. PMID:38435084. |
in this paper, we investigate the potential usefulness of decision transformer (chen et al., 2021)-a new offline rl paradigm-in medical domains where decision making in continuous time is desired. |
2024-03-04 |
2024-03-06 |
Not clear |
| Ke Jiang, Zhaohui Jiang, Xudong Jiang, Yongfang Xie, Weihua Gu. Reinforcement Learning for Blast Furnace Ironmaking Operation With Safety and Partial Observation Considerations. IEEE transactions on neural networks and learning systems. vol PP. 2024-01-17. PMID:38231813. |
recently, reinforcement learning (rl) has demonstrated state-of-the-art performance in various sequential decision-making problems. |
2024-01-17 |
2024-01-20 |
Not clear |
| Floris den Hengst, Martijn Otten, Paul Elbers, Frank van Harmelen, Vincent François-Lavet, Mark Hoogendoor. Guideline-informed reinforcement learning for mechanical ventilation in critical care. Artificial intelligence in medicine. vol 147. 2024-01-06. PMID:38184349. |
reinforcement learning (rl) has recently found many applications in the healthcare domain thanks to its natural fit to clinical decision-making and ability to learn optimal decisions from observational data. |
2024-01-06 |
2024-01-09 |
Not clear |
| Martijn Otten, Ameet R Jagesar, Tariq A Dam, Laurens A Biesheuvel, Floris den Hengst, Kirsten A Ziesemer, Patrick J Thoral, Harm-Jan de Grooth, Armand R J Girbes, Vincent François-Lavet, Mark Hoogendoorn, Paul W G Elber. Does Reinforcement Learning Improve Outcomes for Critically Ill Patients? A Systematic Review and Level-of-Readiness Assessment. Critical care medicine. 2023-11-08. PMID:37938042. |
reinforcement learning (rl) is a machine learning technique uniquely effective at sequential decision-making, which makes it potentially relevant to icu treatment challenges. |
2023-11-08 |
2023-11-20 |
Not clear |
| Enrique Adrian Villarrubia-Martin, Luis Rodriguez-Benitez, Luis Jimenez-Linares, David Muñoz-Valero, Jun Li. A Hybrid Online Off-Policy Reinforcement Learning Agent Framework Supported by Transformers. International journal of neural systems. 2023-10-19. PMID:37857407. |
reinforcement learning (rl) is a powerful technique that allows agents to learn optimal decision-making policies through interactions with an environment. |
2023-10-19 |
2023-11-08 |
Not clear |
| Arslan Musaddiq, Tobias Olsson, Fredrik Ahlgre. Reinforcement-Learning-Based Routing and Resource Management for Internet of Things Environments: Theoretical Perspective and Challenges. Sensors (Basel, Switzerland). vol 23. issue 19. 2023-10-14. PMID:37837093. |
meanwhile, reinforcement learning (rl) has proven to be one of the most effective solutions for decision making. |
2023-10-14 |
2023-10-15 |
Not clear |
| Arslan Musaddiq, Tobias Olsson, Fredrik Ahlgre. Reinforcement-Learning-Based Routing and Resource Management for Internet of Things Environments: Theoretical Perspective and Challenges. Sensors (Basel, Switzerland). vol 23. issue 19. 2023-10-14. PMID:37837093. |
rl holds significant potential for its application in iot device's communication-related decision making, with the goal of improving performance. |
2023-10-14 |
2023-10-15 |
Not clear |
| Sharon M Noh, Umesh K Singla, Ilana J Bennett, Aaron M Bornstei. Memory precision and age differentially predict the use of decision-making strategies across the lifespan. Scientific reports. vol 13. issue 1. 2023-10-09. PMID:37813942. |
recent work has proposed memory sampling as a specific computational role for memory in decision-making, alongside well-studied mechanisms of reinforcement learning (rl). |
2023-10-09 |
2023-10-15 |
human |
| Toby Wise, Kara Emery, Angela Radulesc. Naturalistic reinforcement learning. Trends in cognitive sciences. 2023-09-30. PMID:37777463. |
human cognitive computational neuroscience has sought to exploit reinforcement learning (rl) as a framework within which to explain human decision-making, often focusing on constrained, artificial experimental tasks. |
2023-09-30 |
2023-10-07 |
human |
| Jung In Kim, Young Jae Lee, Jongkook Heo, Jinhyeok Park, Jaehoon Kim, Sae Rin Lim, Jinyong Jeong, Seoung Bum Ki. Sample-efficient multi-agent reinforcement learning with masked reconstruction. PloS one. vol 18. issue 9. 2023-09-14. PMID:37708154. |
deep reinforcement learning (drl) is a powerful approach that combines reinforcement learning (rl) and deep learning to address complex decision-making problems in high-dimensional environments. |
2023-09-14 |
2023-10-07 |
Not clear |
| Erik M Elster, Ruth Pauli, Sarah Baumann, Stephane A De Brito, Graeme Fairchild, Christine M Freitag, Kerstin Konrad, Veit Roessner, Inti A Brazil, Patricia L Lockwood, Gregor Kohl. Impaired Punishment Learning in Conduct Disorder. Journal of the American Academy of Child and Adolescent Psychiatry. 2023-07-06. PMID:37414274. |
conduct disorder (cd) has been associated with deficits in the use of punishment to guide reinforcement learning (rl) and decision making. |
2023-07-06 |
2023-08-14 |
Not clear |
| Tianmeng Hu, Biao Luo, Chunhua Yang, Tingwen Huan. MO-MIX: Multi-Objective Multi-Agent Cooperative Decision-Making With Deep Reinforcement Learning. IEEE transactions on pattern analysis and machine intelligence. vol PP. 2023-06-07. PMID:37285257. |
deep reinforcement learning (rl) has been applied extensively to solve complex decision-making problems. |
2023-06-07 |
2023-08-14 |
Not clear |
| Nour Ben Hassen, Francisco Molins, Mónica Paz, Miguel-Ángel Serran. Later stages of acute stress impair reinforcement-learning and feedback sensitivity in decision making. Biological psychology. 2023-05-13. PMID:37178755. |
the value-plus-preservation (vpp) rl computational model was used to extract decision-making components. |
2023-05-13 |
2023-08-14 |
human |