| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| 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 |
| Shangding Gu, Long Yang, Yali Du, Guang Chen, Florian Walter, Jun Wang, Alois Knol. A Review of Safe Reinforcement Learning: Methods, Theories and Applications. IEEE transactions on pattern analysis and machine intelligence. vol PP. 2024-09-11. PMID:39255180. |
reinforcement learning (rl) has achieved tremendous success in many complex decision-making tasks. |
2024-09-11 |
2024-09-13 |
Not clear |
| Xingche Guo, Donglin Zeng, Yuanjia Wan. HMM for discovering decision-making dynamics using reinforcement learning experiments. Biostatistics (Oxford, England). 2024-09-03. PMID:39226534. |
recent findings suggest the inadequacy of characterizing reward learning solely based on a single rl model; instead, there may be a switching of decision-making processes between multiple strategies. |
2024-09-03 |
2024-09-06 |
human |
| Xingche Guo, Donglin Zeng, Yuanjia Wan. HMM for discovering decision-making dynamics using reinforcement learning experiments. Biostatistics (Oxford, England). 2024-09-03. PMID:39226534. |
our model accommodates decision-making strategy switching between two distinct approaches under an hmm: subjects making decisions based on the rl model or opting for random choices. |
2024-09-03 |
2024-09-06 |
human |
| Parvin Malekzadeh, Konstantinos N Platanioti. Active Inference and Reinforcement Learning: A Unified Inference on Continuous State and Action Spaces under Partial Observability. Neural computation. 2024-08-23. PMID:39177966. |
reinforcement learning (rl) has garnered significant attention for developing decision-making agents that aim to maximize rewards, specified by an external supervisor, within fully observable environments. |
2024-08-23 |
2024-08-25 |
Not clear |
| Meriam Zid, Veldon-James Laurie, Alix Levine-Champagne, Akram Shourkeshti, Dameon Harrell, Alexander B Herman, R Becket Ebit. Humans forage for reward in reinforcement learning tasks. bioRxiv : the preprint server for biology. 2024-07-19. PMID:39026817. |
in order to determine which view better describes human decision-making, we developed a novel, foraging-inspired sequential decision-making model and used it to ask whether humans compare to threshold ("forage") or compare alternatives ("reinforcement-learn" [rl]). |
2024-07-19 |
2024-07-21 |
human |
| Rui Zhao, Ziguo Chen, Yuze Fan, Yun Li, Fei Ga. Towards Robust Decision-Making for Autonomous Highway Driving Based on Safe Reinforcement Learning. Sensors (Basel, Switzerland). vol 24. issue 13. 2024-07-13. PMID:39000919. |
therefore, decision-making based on rl must adequately consider potential variations in data distribution. |
2024-07-13 |
2024-07-15 |
Not clear |
| Elena Maria Tosca, Alessandro De Carlo, Davide Ronchi, Paolo Magn. Model-Informed Reinforcement Learning for Enabling Precision Dosing Via Adaptive Dosing. Clinical pharmacology and therapeutics. 2024-07-11. PMID:38989560. |
reinforcement learning (rl) naturally fits this paradigm: it perfectly mimics the sequential decision-making process where clinicians adapt dose administration based on patient response and evolution monitoring. |
2024-07-11 |
2024-07-13 |
Not clear |
| Ali Shirali, Alexander Schubert, Ahmed Ala. Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care. IEEE journal of biomedical and health informatics. vol PP. 2024-06-18. PMID:38885106. |
this sparsity can reduce the stability of offline estimates, posing a significant hurdle in fully utilizing rl for medical decision-making. |
2024-06-18 |
2024-06-21 |
Not clear |
| Daniel G Dillon, Emily L Belleau, Julianne Origlio, Madison McKee, Aava Jahan, Ashley Meyer, Min Kang Souther, Devon Brunner, Manuel Kuhn, Yuen Siang Ang, Cristina Cusin, Maurizio Fava, Diego A Pizzagall. Using Drift Diffusion and RL Models to Disentangle Effects of Depression On Decision-Making vs. Learning in the Probabilistic Reward Task. Computational psychiatry (Cambridge, Mass.). vol 8. issue 1. 2024-05-23. PMID:38774430. |
using drift diffusion and rl models to disentangle effects of depression on decision-making vs. learning in the probabilistic reward task. |
2024-05-23 |
2024-05-27 |
Not clear |
| Daniel G Dillon, Emily L Belleau, Julianne Origlio, Madison McKee, Aava Jahan, Ashley Meyer, Min Kang Souther, Devon Brunner, Manuel Kuhn, Yuen Siang Ang, Cristina Cusin, Maurizio Fava, Diego A Pizzagall. Using Drift Diffusion and RL Models to Disentangle Effects of Depression On Decision-Making vs. Learning in the Probabilistic Reward Task. Computational psychiatry (Cambridge, Mass.). vol 8. issue 1. 2024-05-23. PMID:38774430. |
the probabilistic reward task (prt) is widely used to investigate the impact of major depressive disorder (mdd) on reinforcement learning (rl), and recent studies have used it to provide insight into decision-making mechanisms affected by mdd. |
2024-05-23 |
2024-05-27 |
Not clear |
| 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 |