| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| 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 |
| 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 |