| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Rui Zhao, Kui Wang, Wenbo Che, Yun Li, Yuze Fan, Fei Ga. Adaptive Cruise Control Based on Safe Deep Reinforcement Learning. Sensors (Basel, Switzerland). vol 24. issue 8. 2024-04-27. PMID:38676274. |
firstly, we transform the acc problem into a safe drl formulation constrained markov decision process (cmdp) by carefully designing state, action, reward, and cost functions. |
2024-04-27 |
2024-04-29 |
Not clear |
| Zihe Liu, Jie Lu, Junyu Xuan, Guangquan Zhan. Deep Reinforcement Learning in Nonstationary Environments With Unknown Change Points. IEEE transactions on cybernetics. vol PP. 2024-02-13. PMID:38349837. |
deep reinforcement learning (drl) is a powerful tool for learning from interactions within a stationary environment where state transition and reward distributions remain constant throughout the process. |
2024-02-13 |
2024-02-16 |
Not clear |
| Zihe Liu, Jie Lu, Junyu Xuan, Guangquan Zhan. Deep Reinforcement Learning in Nonstationary Environments With Unknown Change Points. IEEE transactions on cybernetics. vol PP. 2024-02-13. PMID:38349837. |
addressing the practical but challenging nonstationary environments with time-varying state transition or reward function changes during the interactions, ingenious solutions are essential for the stability and robustness of drl agents. |
2024-02-13 |
2024-02-16 |
Not clear |
| Haoyu Cheng, Ruijia Song, Haoran Li, Wencheng Wei, Biyu Zheng, Yangwang Fan. Realizing asynchronous finite-time robust tracking control of switched flight vehicles by using nonfragile deep reinforcement learning. Frontiers in neuroscience. vol 17. 2024-01-08. PMID:38188035. |
unlike the conventional drl algorithm, nonfragile control theory and adaptive reward function were used in the proposed algorithm to achieve excellent stability and training efficiency. |
2024-01-08 |
2024-01-10 |
Not clear |
| Kabirat Bolanle Olayemi, Mien Van, Sean McLoone, Stephen McIlvanna, Yuzhu Sun, Jack Close, Nhat Minh Nguye. The Impact of LiDAR Configuration on Goal-Based Navigation within a Deep Reinforcement Learning Framework. Sensors (Basel, Switzerland). vol 23. issue 24. 2023-12-23. PMID:38139578. |
the cost function of collision avoidance and path planning is defined as the reward of the drl model. |
2023-12-23 |
2023-12-25 |
Not clear |
| Jiaping Xiao, Phumrapee Pisutsin, Mir Feroskha. Collaborative Target Search With a Visual Drone Swarm: An Adaptive Curriculum Embedded Multistage Reinforcement Learning Approach. IEEE transactions on neural networks and learning systems. vol PP. 2023-11-20. PMID:37983144. |
in this work, we propose a novel data-efficient deep reinforcement learning (drl) approach called adaptive curriculum embedded multistage learning (acemsl) to address these challenges, mainly 3-d sparse reward space exploration with limited visual perception and collaborative behavior requirements. |
2023-11-20 |
2023-11-29 |
Not clear |
| Tassneem Zamzam, Khaled Shaban, Ahmed Massou. Optimal Reactive Power Dispatch in ADNs using DRL and the Impact of Its Various Settings and Environmental Changes. Sensors (Basel, Switzerland). vol 23. issue 16. 2023-08-26. PMID:37631753. |
to this end, in this paper we examine the impact of various drl reward representations and hyperparameters on the agent's learning performance when solving the orpd problem for adns. |
2023-08-26 |
2023-09-07 |
Not clear |
| Tianyu Xing, Xiaohao Wang, Kaiyang Ding, Kai Ni, Qian Zho. Improved Artificial Potential Field Algorithm Assisted by Multisource Data for AUV Path Planning. Sensors (Basel, Switzerland). vol 23. issue 15. 2023-08-12. PMID:37571463. |
the improved artificial potential field (iapf) method is combined with drl for path planning while optimizing the reward function in the drl algorithm and using the generated path to optimize the future path. |
2023-08-12 |
2023-08-16 |
Not clear |
| Suchen Li, Zhuo Tang, Lifang Yang, Mengmeng Li, Zhigang Shan. Application of deep reinforcement learning for spike sorting under multi-class imbalance. Computers in biology and medicine. vol 164. 2023-08-03. PMID:37536094. |
since deep reinforcement learning (drl) assign targeted attention to categories through reward functions, we propose imbsorter to implement spike sorting under multi-class imbalance. |
2023-08-03 |
2023-08-14 |
monkey |
| Yingze Wang, Mengying Sun, Qimei Cui, Kwang-Cheng Chen, Yaxin Lia. RIS-Aided Proactive Mobile Network Downlink Interference Suppression: A Deep Reinforcement Learning Approach. Sensors (Basel, Switzerland). vol 23. issue 14. 2023-07-29. PMID:37514846. |
furthermore, motivated by recent developments in deep reinforcement learning (drl), we propose an asynchronous advantage actor-critic (a3c)-based method for solving the problem by appropriately designing the action space, state space, and reward function. |
2023-07-29 |
2023-08-14 |
Not clear |
| Herbert Y H Hui, An Ran Ran, Jia Jia Dai, Carol Y Cheun. Deep Reinforcement Learning-Based Retinal Imaging in Alzheimer's Disease: Potential and Perspectives. Journal of Alzheimer's disease : JAD. 2023-05-22. PMID:37212112. |
challenges and future directions, such as the use of inverse drl in defining reward function, lack of standardization in retinal imaging, and data availability, will also be addressed to bridge gaps for its transition into clinical use. |
2023-05-22 |
2023-08-14 |
Not clear |
| Dunxing Long, Qiong Wu, Qiang Fan, Pingyi Fan, Zhengquan Li, Jing Fa. A Power Allocation Scheme for MIMO-NOMA and D2D Vehicular Edge Computing Based on Decentralized DRL. Sensors (Basel, Switzerland). vol 23. issue 7. 2023-04-13. PMID:37050509. |
extensive experiments demonstrate that our proposed approach with drl and ddpg outperforms existing greedy strategies in terms of power consumption and reward. |
2023-04-13 |
2023-08-14 |
Not clear |
| Shuyue Chen, Ran Wang, Jian L. A meta-framework for multi-label active learning based on deep reinforcement learning. Neural networks : the official journal of the International Neural Network Society. vol 162. 2023-03-13. PMID:36913822. |
in addition, a self-attention mechanism along with a reward function is integrated into the drl structure to address the label correlation and data imbalanced problems in mlal. |
2023-03-13 |
2023-08-14 |
Not clear |
| Chengqing Liang, Lei Liu, Chen Li. Multi-UAV autonomous collision avoidance based on PPO-GIC algorithm with CNN-LSTM fusion network. Neural networks : the official journal of the International Neural Network Society. vol 162. 2023-03-06. PMID:36878168. |
an end-to-end deep reinforcement learning (drl) control strategy and a potential-based reward function are designed. |
2023-03-06 |
2023-08-14 |
Not clear |
| Shunsuke Koseki, Kyo Kutsuzawa, Dai Owaki, Mitsuhiro Hayashib. Multimodal bipedal locomotion generation with passive dynamics Frontiers in neurorobotics. vol 16. 2023-02-09. PMID:36756534. |
by carefully planning the weight parameter settings of the drl reward function during the learning process based on a curriculum learning method, the bipedal model successfully learned to walk, run, and perform gait transitions by adjusting only one command input. |
2023-02-09 |
2023-08-14 |
Not clear |
| 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. |
this article proposes a framework based on deep reinforcement learning (drl) using scale invariant faster region-based convolutional neural networks (sifrcnn) technologies to efficiently detect pedestrian operations through which the vehicle, as agents train themselves from the environment and are forced to maximize the reward. |
2023-01-21 |
2023-08-14 |
Not clear |
| Jichiang Tsai, Che-Cheng Chang, Tzu L. Autonomous Driving Control Based on the Technique of Semantic Segmentation. Sensors (Basel, Switzerland). vol 23. issue 2. 2023-01-21. PMID:36679688. |
moreover, we also design an appropriate reward mechanism for these drl models to realize efficient autonomous driving control. |
2023-01-21 |
2023-08-14 |
Not clear |
| Lilia Tightiz, Joon Yo. A novel deep reinforcement learning based business model arrangement for Korean net-zero residential micro-grid considering whole stakeholders' interests. ISA transactions. 2022-12-17. PMID:36528394. |
due to the implementation of mixed-integer nonlinear programming (minlp) to solve the reward function in this paper, ddqn, despite other drl methods, provides precise, explicit, and meaningful rewards. |
2022-12-17 |
2023-08-14 |
Not clear |
| Reinis Cimurs, Emmanuel Alejandro Merchán-Cru. Leveraging Expert Demonstration Features for Deep Reinforcement Learning in Floor Cleaning Robot Navigation. Sensors (Basel, Switzerland). vol 22. issue 20. 2022-10-27. PMID:36298101. |
a reward function is created based on the feature values to train a policy with semi-supervised drl, where an immediate reward is calculated based on the closeness to the expert navigation. |
2022-10-27 |
2023-08-14 |
Not clear |
| Lanyu Xu, Simeng Zhu, Ning We. Deep reinforcement learning and its applications in medical imaging and radiation therapy: a survey. Physics in medicine and biology. 2022-10-21. PMID:36270582. |
drl algorithms, by fostering the designs of reward function, agents interactions and environment models, can resolve the challenges from scarce and heterogeneous annotated medical image data, which has been a major obstacle to implementing deep learning models in the clinic. |
2022-10-21 |
2023-08-14 |
Not clear |