| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Qihao Wu, Jiangxue Han, Yimo Yan, Yong-Hong Kuo, Zuo-Jun Max She. Reinforcement learning for healthcare operations management: methodological framework, recent developments, and future research directions. Health care management science. 2025-04-09. PMID:40202690. |
with the advancement in computing power and data science techniques, reinforcement learning (rl) has emerged as a powerful tool for decision-making problems in complex systems. |
2025-04-09 |
2025-04-11 |
Not clear |
| Kairui Feng, Ning Lin, Robert E Kopp, Siyuan Xian, Michael Oppenheime. Reinforcement learning-based adaptive strategies for climate change adaptation: An application for coastal flood risk management. Proceedings of the National Academy of Sciences of the United States of America. vol 122. issue 12. 2025-03-18. PMID:40100629. |
here, we investigate the potential of reinforcement learning (rl), a machine learning technique that efficaciously acquires knowledge from the environment and systematically optimizes dynamic decisions, in modeling and informing adaptive climate decision-making. |
2025-03-18 |
2025-03-21 |
Not clear |
| Affan Affan, Tamer Inan. Learning Enabled Control for Optimal EPO Dosage in Virtual CKD Patients: Case of Bleeding and Missing Dosage. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. vol 2024. 2025-03-05. PMID:40039101. |
the simulation results show that ampc and dqn-rl both can provide optimal results while meeting the constraints, however, dqn-rl is more computationally challenging and demands high data.clinical relevance- this research work provides a framework to help clinicians in decision-making for personalized epo dose guidance using model-free reinforcement learning (rl) and adaptive model predictive control (ampc) in the events of bleeding and missing dosages. |
2025-03-05 |
2025-03-07 |
Not clear |
| Yan Zeng, Ruichu Cai, Fuchun Sun, Libo Huang, Zhifeng Ha. A Survey on Causal Reinforcement Learning. IEEE transactions on neural networks and learning systems. vol PP. 2025-03-03. PMID:40030342. |
while reinforcement learning (rl) achieves tremendous success in sequential decision-making problems of many domains, it still faces key challenges of data inefficiency and the lack of interpretability. |
2025-03-03 |
2025-03-14 |
Not clear |
| Jingliang Duan, Wenxuan Wang, Liming Xiao, Jiaxin Gao, Shengbo Eben Li, Chang Liu, Ya-Qin Zhang, Bo Cheng, Keqiang L. Distributional Soft Actor-Critic With Three Refinements. IEEE transactions on pattern analysis and machine intelligence. vol PP. 2025-03-03. PMID:40031258. |
reinforcement learning (rl) has shown remarkable success in solving complex decision-making and control tasks. |
2025-03-03 |
2025-03-06 |
Not clear |
| Peyman Ghasemi, Matthew Greenberg, Danielle A Southern, Bing Li, James A White, Joon Le. Personalized decision making for coronary artery disease treatment using offline reinforcement learning. NPJ digital medicine. vol 8. issue 1. 2025-02-13. PMID:39948243. |
our findings indicate that rl-guided therapy decisions outperformed physician-based decision making, with rl policies achieving up to 32% improvement in expected rewards based on composite major cardiovascular events outcomes. |
2025-02-13 |
2025-02-16 |
Not clear |
| Christina E Wierenga, Amanda Bischoff-Grethe, Carina S Brown, Gregory G Brow. Reinforcement learning in women remitted from anorexia nervosa: Preliminary examination with a hybrid reinforcement learning/drift diffusion model. Journal of the International Neuropsychological Society : JINS. 2025-02-03. PMID:39895119. |
altered reinforcement learning (rl) and decision-making have been implicated in the pathophysiology of anorexia nervosa. |
2025-02-03 |
2025-02-05 |
Not clear |
| Erik M Elster, Ruth Pauli, Graeme Fairchild, Maria McDonald, Sarah Baumann, Justina Sidlauskaite, Stephane De Brito, Christine M Freitag, Kerstin Konrad, Veit Roessner, Inti A Brazil, Patricia L Lockwood, Gregor Kohl. Altered Neural Responses to Punishment Learning in Conduct Disorder. Biological psychiatry. Cognitive neuroscience and neuroimaging. 2025-01-13. PMID:39805552. |
conduct disorder (cd) is associated with deficits in the use of punishment for reinforcement learning (rl) and subsequent decision-making, contributing to reckless, antisocial, and aggressive behaviors. |
2025-01-13 |
2025-01-16 |
Not clear |
| Rui Zhao, Yuze Fan, Yun Li, Dong Zhang, Fei Gao, Zhenhai Gao, Zhengcai Yan. Knowledge Distillation-Enhanced Behavior Transformer for Decision-Making of Autonomous Driving. Sensors (Basel, Switzerland). vol 25. issue 1. 2025-01-11. PMID:39796987. |
imitation learning (il) and reinforcement learning (rl) have introduced innovative approaches to behavior decision-making in autonomous driving, but challenges remain. |
2025-01-11 |
2025-01-14 |
Not clear |
| Rui Zhao, Yuze Fan, Yun Li, Dong Zhang, Fei Gao, Zhenhai Gao, Zhengcai Yan. Knowledge Distillation-Enhanced Behavior Transformer for Decision-Making of Autonomous Driving. Sensors (Basel, Switzerland). vol 25. issue 1. 2025-01-11. PMID:39796987. |
building on the successful application of transformers in large language models, we introduce the behavior transformer as the policy network in rl, using observation-action history as input for sequential decision-making, thereby leveraging the transformer's contextual reasoning capabilities. |
2025-01-11 |
2025-01-14 |
Not clear |
| Chiara Montemitro, Paolo Ossola, Thomas J Ross, Quentin J M Huys, John R Fedota, Betty Jo Salmeron, Massimo di Giannantonio, Elliot A Stei. Longitudinal changes in reinforcement learning during smoking cessation: a computational analysis using a probabilistic reward task. Scientific reports. vol 14. issue 1. 2024-12-31. PMID:39741189. |
based on preregistered hypotheses and analysis plan ( https://osf.io/yq5th ), we examined the evolution of reinforcement learning (rl), a key component of decision-making, in smokers during acute and extended nicotine abstinence. |
2024-12-31 |
2025-01-03 |
Not clear |
| Chiara Montemitro, Paolo Ossola, Thomas J Ross, Quentin J M Huys, John R Fedota, Betty Jo Salmeron, Massimo di Giannantonio, Elliot A Stei. Longitudinal changes in reinforcement learning during smoking cessation: a computational analysis using a probabilistic reward task. Scientific reports. vol 14. issue 1. 2024-12-31. PMID:39741189. |
we evaluated changes in reward-based decision-making using signal-detection analysis and five rl models across three sessions during 30 days of nicotine abstinence. |
2024-12-31 |
2025-01-03 |
Not clear |
| Xingyue Liang, Qiaoyun Wu, Wenzhang Liu, Yun Zhou, Chunyu Tan, Hongfu Yin, Changyin Su. Intrinsic plasticity coding improved spiking actor network for reinforcement learning. Neural networks : the official journal of the International Neural Network Society. vol 184. 2024-12-28. PMID:39732066. |
we propose an intrinsic plasticity coding improved spiking actor network (ip-san) for rl to achieve effective decision-making. |
2024-12-28 |
2024-12-31 |
Not clear |
| Pushkala Jayaraman, Jacob Desman, Moein Sabounchi, Girish N Nadkarni, Ankit Sakhuj. A Primer on Reinforcement Learning in Medicine for Clinicians. NPJ digital medicine. vol 7. issue 1. 2024-11-26. PMID:39592855. |
reinforcement learning (rl) is a machine learning paradigm that enhances clinical decision-making for healthcare professionals by addressing uncertainties and optimizing sequential treatment strategies. |
2024-11-26 |
2024-11-29 |
Not clear |
| Vanessa Scholz, Maria Waltmann, Nadine Herzog, Annette Horstmann, Lorenz Desern. Decrease in decision noise from adolescence into adulthood mediates an increase in more sophisticated choice behaviors and performance gain. PLoS biology. vol 22. issue 11. 2024-11-14. PMID:39541313. |
to answer this, we examined 93 participants (12 to 42 years) who completed 3 reinforcement learning (rl) tasks: a motivational go/nogo task assessing motivational influences over choices, a reversal learning task capturing adaptive decision-making in response to environmental changes, and a sequential choice task measuring goal-directed behavior. |
2024-11-14 |
2024-11-17 |
human |
| Kengo Shibata, Verena Klar, Sean J Fallon, Masud Husain, Sanjay G Manoha. Working memory as a representational template for reinforcement learning. Scientific reports. vol 14. issue 1. 2024-11-12. PMID:39532969. |
working memory (wm) and reinforcement learning (rl) both influence decision-making, but how they interact to affect behaviour remains unclear. |
2024-11-12 |
2024-11-17 |
human |
| Botao Dong, Longyang Huang, Ning Pang, Hongtian Chen, Weidong Zhan. Historical Decision-Making Regularized Maximum Entropy Reinforcement Learning. IEEE transactions on neural networks and learning systems. vol PP. 2024-10-29. PMID:39471125. |
to tackle this challenge, a novel historical decision-making regularized maximum entropy (hdmrme) rl algorithm is developed to strike the balance between exploration and exploitation. |
2024-10-29 |
2024-11-02 |
Not clear |
| Botao Dong, Longyang Huang, Ning Pang, Hongtian Chen, Weidong Zhan. Historical Decision-Making Regularized Maximum Entropy Reinforcement Learning. IEEE transactions on neural networks and learning systems. vol PP. 2024-10-29. PMID:39471125. |
built upon the maximum entropy rl framework, the historical decision-making regularization method is proposed to enhance the exploitation capability of rl policies. |
2024-10-29 |
2024-11-02 |
Not clear |
| Francisco Molins, Nour Ben Hassen, Miguel Ángel Serran. Late Acute Stress Effects on Decision-Making: The Magnified Attraction to Immediate Gains in the Iowa Gambling Task. Behavioural brain research. 2024-10-04. PMID:39366556. |
employing the value-plus-perseveration (vpp) rl model, based on bayesian logic, this study aims to gain specific insights into how late phase of acute stress impacts the cognitive processes underpinning decision-making in the iowa gambling task (igt), deciphering whether, as expected, gains are processed in a magnified manner. |
2024-10-04 |
2024-10-07 |
human |
| Jinna Li, Lin Yuan, Weiran Cheng, Tianyou Chai, Frank L Lewi. Reinforcement Learning for Synchronization of Heterogeneous Multiagent Systems by Improved Q -Functions. IEEE transactions on cybernetics. vol PP. 2024-09-24. PMID:39316502. |
in the developed mechanism, an improved q -function with an arbitration factor is designed for accommodating the fact that control protocols tend to be made by historic experiences and instinctive decision-making, such that the degree of control over agents' behaviors can be adaptively allocated by on-policy and off-policy rl techniques for the optimal multiagent synchronization problem. |
2024-09-24 |
2024-09-27 |
Not clear |