| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Wenjie Shi, Gao Huang, Shiji Song, Zhuoyuan Wang, Tingyu Lin, Cheng W. Self-Supervised Discovering of Interpretable Features for Reinforcement Learning. IEEE transactions on pattern analysis and machine intelligence. vol PP. 2021-05-19. PMID:33186101. |
overall, our method provides valuable insight into the decision-making process of rl. |
2021-05-19 |
2023-08-13 |
Not clear |
| Rick A Adams, Michael Moutoussis, Matthew M Nour, Tarik Dahoun, Declan Lewis, Benjamin Illingworth, Mattia Veronese, Christoph Mathys, Lieke de Boer, Marc Guitart-Masip, Karl J Friston, Oliver D Howes, Jonathan P Roise. Variability in Action Selection Relates to Striatal Dopamine 2/3 Receptor Availability in Humans: A PET Neuroimaging Study Using Reinforcement Learning and Active Inference Models. Cerebral cortex (New York, N.Y. : 1991). vol 30. issue 6. 2021-05-04. PMID:32083297. |
we investigated this mechanism using two influential decision-making frameworks: active inference (ai) and reinforcement learning (rl). |
2021-05-04 |
2023-08-13 |
human |
| Xiaohan Zhang, Lu Liu, Guodong Long, Jing Jiang, Shenquan Li. Episodic memory governs choices: An RNN-based reinforcement learning model for decision-making task. Neural networks : the official journal of the International Neural Network Society. vol 134. 2021-03-23. PMID:33276194. |
to alleviate this problem, we develop an rnn-based actor-critic framework, which is trained through reinforcement learning (rl) to solve two tasks analogous to the monkeys' decision-making tasks. |
2021-03-23 |
2023-08-13 |
monkey |
| Elaine Aparecida Regiani de Campos, Madjid Tavana, Carla Schwengber Ten Caten, Marina Bouzon, Istefani Carísio de Paul. A grey-DEMATEL approach for analyzing factors critical to the implementation of reverse logistics in the pharmaceutical care process. Environmental science and pollution research international. vol 28. issue 11. 2021-03-18. PMID:33206293. |
we use snowball sampling to select the relevant rl studies and deductive reasoning and classification to identify the critical factors and a grey decision-making trial and evaluation laboratory (dematel) to evaluate the cause-and-effect relationships among them. |
2021-03-18 |
2023-08-13 |
Not clear |
| Steven Miletić, Russell J Boag, Anne C Trutti, Niek Stevenson, Birte U Forstmann, Andrew Heathcot. A new model of decision processing in instrumental learning tasks. eLife. vol 10. 2021-02-17. PMID:33501916. |
recently, evidence accumulation models (eams) of decision-making and reinforcement learning (rl) models of error-driven learning have been combined into joint rl-eams that can in principle address these interactions. |
2021-02-17 |
2023-08-13 |
Not clear |
| Chao Yu, Guoqi Ren, Yinzhao Don. Supervised-actor-critic reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units. BMC medical informatics and decision making. vol 20. issue Suppl 3. 2021-02-15. PMID:32646412. |
reinforcement learning (rl) provides a promising technique to solve complex sequential decision making problems in healthcare domains. |
2021-02-15 |
2023-08-13 |
Not clear |
| Chao Yu, Guoqi Ren, Yinzhao Don. Supervised-actor-critic reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units. BMC medical informatics and decision making. vol 20. issue Suppl 3. 2021-02-15. PMID:32646412. |
recent years have seen a great progress of applying rl in addressing decision-making problems in intensive care units (icus). |
2021-02-15 |
2023-08-13 |
Not clear |
| Matthew Botvinick, Jane X Wang, Will Dabney, Kevin J Miller, Zeb Kurth-Nelso. Deep Reinforcement Learning and Its Neuroscientific Implications. Neuron. vol 107. issue 4. 2020-11-02. PMID:32663439. |
deep rl offers a comprehensive framework for studying the interplay among learning, representation, and decision making, offering to the brain sciences a new set of research tools and a wide range of novel hypotheses. |
2020-11-02 |
2023-08-13 |
Not clear |
| Zahra Mahmoodzadeh, Keo-Yuan Wu, Enrique Lopez Droguett, Ali Mosle. Condition-Based Maintenance with Reinforcement Learning for Dry Gas Pipeline Subject to Internal Corrosion. Sensors (Basel, Switzerland). vol 20. issue 19. 2020-10-29. PMID:33036494. |
second, we propose a condition-based maintenance management approach that leverages a data-driven rl decision-making methodology. |
2020-10-29 |
2023-08-13 |
human |
| Adam Morris, Fiery Cushma. Model-Free RL or Action Sequences? Frontiers in psychology. vol 10. 2020-10-01. PMID:31920900. |
the alignment of habits with model-free reinforcement learning (mf rl) is a success story for computational models of decision making, and mf rl has been applied to explain phasic dopamine responses (schultz et al., 1997), working memory gating (o'reilly and frank, 2006), drug addiction (redish, 2004), moral intuitions (crockett, 2013; cushman, 2013), and more. |
2020-10-01 |
2023-08-13 |
human |
| Adam Morris, Fiery Cushma. Model-Free RL or Action Sequences? Frontiers in psychology. vol 10. 2020-10-01. PMID:31920900. |
here, we present two experiments that dissociate mf rl from this prominent alternative, and present unconfounded empirical support for the role of mf rl in human decision making. |
2020-10-01 |
2023-08-13 |
human |
| Cameron D Hassall, Greg Hajcak, Olave E Krigolso. The importance of agency in human reward processing. Cognitive, affective & behavioral neuroscience. vol 19. issue 6. 2020-09-28. PMID:31187443. |
according to rl theory, prediction errors are used to update values associated with actions and/or predictive cues, thus facilitate decision-making. |
2020-09-28 |
2023-08-13 |
human |
| Jeremy A Metha, Maddison L Brian, Sara Oberrauch, Samuel A Barnes, Travis J Featherby, Peter Bossaerts, Carsten Murawski, Daniel Hoyer, Laura H Jacobso. Separating Probability and Reversal Learning in a Novel Probabilistic Reversal Learning Task for Mice. Frontiers in behavioral neuroscience. vol 13. 2020-09-28. PMID:31998088. |
this new decision task, coupled with rl analyses, advances access to investigate the neuroscience of the exploration/exploitation tradeoff in decision making. |
2020-09-28 |
2023-08-13 |
mouse |
| Yaser Keneshloo, Tian Shi, Naren Ramakrishnan, Chandan K Redd. Deep Reinforcement Learning for Sequence-to-Sequence Models. IEEE transactions on neural networks and learning systems. vol 31. issue 7. 2020-09-14. PMID:31425057. |
in this survey, we consider seq2seq problems from the rl point of view and provide a formulation combining the power of rl methods in decision-making with seq2seq models that enable remembering long-term memories. |
2020-09-14 |
2023-08-13 |
Not clear |
| Thanh Thi Nguyen, Ngoc Duy Nguyen, Saeid Nahavand. Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications. IEEE transactions on cybernetics. vol 50. issue 9. 2020-08-19. PMID:32203045. |
reinforcement learning (rl) algorithms have been around for decades and employed to solve various sequential decision-making problems. |
2020-08-19 |
2023-08-13 |
Not clear |
| Chao Yu, Jiming Liu, Hongyi Zha. Inverse reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units. BMC medical informatics and decision making. vol 19. issue Suppl 2. 2020-01-14. PMID:30961594. |
reinforcement learning (rl) provides a promising technique to solve complex sequential decision making problems in health care domains. |
2020-01-14 |
2023-08-13 |
Not clear |
| Chao Yu, Yinzhao Dong, Jiming Liu, Guoqi Re. Incorporating causal factors into reinforcement learning for dynamic treatment regimes in HIV. BMC medical informatics and decision making. vol 19. issue Suppl 2. 2020-01-14. PMID:30961606. |
reinforcement learning (rl) provides a promising technique to solve complex sequential decision making problems in health care domains. |
2020-01-14 |
2023-08-13 |
Not clear |
| Dongbin Zhao, Derong Liu, F L Lewis, Jose C Principe, Stefanoi Squarti. Editorial Special Issue on Deep Reinforcement Learning and Adaptive Dynamic Programming. IEEE transactions on neural networks and learning systems. 2019-11-20. PMID:29993895. |
deep rl is able to output control signal directly based on input images, which incorporates both the advantages of the perception of deep learning (dl) and the decision making of rl or adaptive dynamic programming (adp). |
2019-11-20 |
2023-08-13 |
human |
| Vincent Moens, Alexandre Zéno. Learning and forgetting using reinforced Bayesian change detection. PLoS computational biology. vol 15. issue 4. 2019-05-28. PMID:30995214. |
overall, the proposed method provides a general framework to study learning flexibility and decision making in rl contexts. |
2019-05-28 |
2023-08-13 |
Not clear |
| Wei James Chen, Ian Krajbic. Computational modeling of epiphany learning. Proceedings of the National Academy of Sciences of the United States of America. vol 114. issue 18. 2018-04-24. PMID:28416682. |
models of reinforcement learning (rl) are prevalent in the decision-making literature, but not all behavior seems to conform to the gradual convergence that is a central feature of rl. |
2018-04-24 |
2023-08-13 |
human |