| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Shulei Ji, Xinyu Yang, Jing Luo, Juan L. RL-Chord: CLSTM-Based Melody Harmonization Using Deep Reinforcement Learning. IEEE transactions on neural networks and learning systems. vol PP. 2023-04-07. PMID:37028328. |
specifically, a melody conditional lstm (clstm) model is put forward that learns the transition and duration of chords well, based on which rl algorithms with three well-designed reward modules are combined to construct rl-chord. |
2023-04-07 |
2023-08-14 |
Not clear |
| Ke Lin, Duantengchuan Li, Yanjie Li, Shiyu Chen, Qi Liu, Jianqi Gao, Yanrui Jin, Liang Gon. TAG: Teacher-Advice Mechanism With Gaussian Process for Reinforcement Learning. IEEE transactions on neural networks and learning systems. vol PP. 2023-04-06. PMID:37023165. |
considerable experiments on sparse reward environments demonstrate that the tag mechanism can help typical rl algorithms achieve significant performance gains. |
2023-04-06 |
2023-08-14 |
Not clear |
| Alana Jaskir, Michael J Fran. On the normative advantages of dopamine and striatal opponency for learning and choice. eLife. vol 12. 2023-03-22. PMID:36946371. |
this efficient coding mechanism avoids a vexing explore-exploit tradeoff that plagues traditional rl models in sparse reward environments. |
2023-03-22 |
2023-08-14 |
Not clear |
| Mariam Ibrahim, Ruba Elhafi. Security Analysis of Cyber-Physical Systems Using Reinforcement Learning. Sensors (Basel, Switzerland). vol 23. issue 3. 2023-02-11. PMID:36772676. |
in particular, the state action reward state action (sarsa) rl technique is used, in which the agent is taken to be the attacker, and an attack graph created for the system is built to resemble the environment. |
2023-02-11 |
2023-08-14 |
Not clear |
| C A Hales, L Clark, C A Winstanle. Computational approaches to modeling gambling behaviour: opportunities for understanding disordered gambling. Neuroscience and biobehavioral reviews. 2023-02-09. PMID:36758827. |
rl models focus on explaining how an agent uses reward to learn about the environment and make decisions based on outcomes. |
2023-02-09 |
2023-08-14 |
Not clear |
| Mingyu Cai, Shaoping Xiao, Junchao Li, Zhen Ka. Safe reinforcement learning under temporal logic with reward design and quantum action selection. Scientific reports. vol 13. issue 1. 2023-02-03. PMID:36732441. |
furthermore, a reward shaping process is developed to avoid sparse rewards and enforce the rl convergence while keeping the optimal policies invariant. |
2023-02-03 |
2023-08-14 |
Not clear |
| Rex G Liu, Michael J Fran. Hierarchical clustering optimizes the tradeoff between compositionality and expressivity of task structures for flexible reinforcement learning. Artificial intelligence. vol 312. 2023-01-30. PMID:36711165. |
in rl, these correspond to the reward function and transition function, respectively. |
2023-01-30 |
2023-08-14 |
human |
| Rex G Liu, Michael J Fran. Hierarchical clustering optimizes the tradeoff between compositionality and expressivity of task structures for flexible reinforcement learning. Artificial intelligence. vol 312. 2023-01-30. PMID:36711165. |
prior theoretical work has explored how, in model-based rl, agents can learn and generalize task components (transition and reward functions). |
2023-01-30 |
2023-08-14 |
human |
| Muhammad Shoaib Farooq, Haris Khalid, Ansif Arooj, Tariq Umer, Aamer Bilal Asghar, Jawad Rasheed, Raed M Shubair, Amani Yahyaou. A Conceptual Multi-Layer Framework for the Detection of Nighttime Pedestrian in Autonomous Vehicles Using Deep Reinforcement Learning. Entropy (Basel, Switzerland). vol 25. issue 1. 2023-01-21. PMID:36673276. |
furthermore, we have used reinforcement learning (rl) for optimizing the q-values and training itself to maximize the reward after getting the state from the sifrcnn. |
2023-01-21 |
2023-08-14 |
Not clear |
| Fei-Yang Huang 黃飛揚, Fabian Grabenhors. Nutrient-sensitive reinforcement learning in monkeys. The Journal of neuroscience : the official journal of the Society for Neuroscience. 2023-01-20. PMID:36669886. |
in reinforcement learning (rl), animals choose by assigning values to options and learn by updating these values from reward outcomes. |
2023-01-20 |
2023-08-14 |
monkey |
| Fei-Yang Huang 黃飛揚, Fabian Grabenhors. Nutrient-sensitive reinforcement learning in monkeys. The Journal of neuroscience : the official journal of the Society for Neuroscience. 2023-01-20. PMID:36669886. |
however, canonical rl models do not explain how reward values are constructed from biologically critical intrinsic reward components, such as nutrients. |
2023-01-20 |
2023-08-14 |
monkey |
| Fei-Yang Huang 黃飛揚, Fabian Grabenhors. Nutrient-sensitive reinforcement learning in monkeys. The Journal of neuroscience : the official journal of the Society for Neuroscience. 2023-01-20. PMID:36669886. |
here, to advance the biological and ecological validity of rl models, we investigated how (male) monkeys adapt their choices to obtain preferred nutrient rewards under varying reward probabilities. |
2023-01-20 |
2023-08-14 |
monkey |
| Kshitij Mishra, Mauajama Firdaus, Asif Ekba. GenPADS: Reinforcing politeness in an end-to-end dialogue system. PloS one. vol 18. issue 1. 2023-01-06. PMID:36607963. |
we then incorporate both of these models in a reinforcement learning (rl) setting using two different politeness oriented reward algorithms to adapt and generate polite responses. |
2023-01-06 |
2023-08-14 |
human |
| Yunyao Xie, Longwen Huang, Alberto Corona, Alexa H Pagliaro, Stephen D She. A dopaminergic reward prediction error signal shapes maternal behavior in mice. Neuron. 2022-12-21. PMID:36543170. |
we conclude that this component of maternal behavior is shaped by an rl mechanism in which social contact itself is the primary reward. |
2022-12-21 |
2023-08-14 |
mouse |
| Omer San, Suraj Pawar, Adil Rashee. Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems. Scientific reports. vol 12. issue 1. 2022-10-26. PMID:36289290. |
however, a key element in creating a robust rl agent is to introduce a feasible reward function, which can be constituted of any difference metrics between the rl model and high fidelity simulation data. |
2022-10-26 |
2023-08-14 |
Not clear |
| Omer San, Suraj Pawar, Adil Rashee. Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems. Scientific reports. vol 12. issue 1. 2022-10-26. PMID:36289290. |
we then formulate a variational multiscale rl (vmrl) approach to discover closure models without requiring access to the high fidelity data in designing the reward function. |
2022-10-26 |
2023-08-14 |
Not clear |
| Yanmei Sun, Nian He, Qi Yuan, Yufei Wang, Yan Dong, Dianzhong We. Ferroelectric Polarized in Transistor Channel Polarity Modulation for Reward-Modulated Spike-Time-Dependent Plasticity Application. The journal of physical chemistry letters. 2022-10-20. PMID:36264655. |
reward signals reflect the developmental tendency of reinforcement learning (rl) agents. |
2022-10-20 |
2023-08-14 |
Not clear |
| MyeongSeop Kim, Jung-Su Kim, Myoung-Su Choi, Jae-Han Par. Adaptive Discount Factor for Deep Reinforcement Learning in Continuing Tasks with Uncertainty. Sensors (Basel, Switzerland). vol 22. issue 19. 2022-10-14. PMID:36236366. |
reinforcement learning (rl) trains an agent by maximizing the sum of a discounted reward. |
2022-10-14 |
2023-08-14 |
Not clear |
| Runyu Xu, Xiaogang Ruan, Jing Huan. A Brain-Inspired Model of Hippocampal Spatial Cognition Based on a Memory-Replay Mechanism. Brain sciences. vol 12. issue 9. 2022-09-23. PMID:36138911. |
the experimental results show that under the same conditions, our model has a higher rate of environmental exploration and more stable signal transmission, and the average reward obtained under stable conditions was 14.12% higher than rl with random-experience replay. |
2022-09-23 |
2023-08-14 |
Not clear |
| Benton Girdler, William Caldbeck, Jihye Ba. Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review. Frontiers in systems neuroscience. vol 16. 2022-09-12. PMID:36090185. |
our primary focus in this review is to provide a technical summary of various algorithms used in rl-based bmis to decode neural intention, without emphasizing preprocessing techniques on the neural signals and reward modeling for rl. |
2022-09-12 |
2023-08-14 |
Not clear |