Results 251 to 260 of about 171,350 (313)

A Q-Learning Algorithm with Continuous State Space [PDF]

, 2006
Kengy Barty, Pierre Girardeau, Jean‐Sébastien Roy, Cyrille Strugarek   +3 more
openalex  

1P1-A04 Acquisition of Grasp Planning Based on Q-learning

, 2008
Haruyasu KATO, Hiroki Inoue, Motoki Takagi, Yoshiyuki Takahashi, Takashi Komeda   +4 more
openalex   +2 more sources

Self-Learning Visual Servoing of Robot Manipulator Using Explanation-Based Fuzzy Neural Networks and Q-Learning [PDF]

, 2014
Mehdi Sadeghzadeh, David Calvert, Hussein A. Abdullah   +2 more
openalex   +1 more source

A Multi‐Objective Molecular Generation Method Based on Pareto Algorithm and Monte Carlo Tree Search

Advanced Science, EarlyView.
Pareto Monte Carlo Tree Search Molecular Generation (PMMG), a molecular generation approach leveraging Monte Carlo Tree Search (MCTS) and Pareto algorithm, efficiently explores the Pareto front in high‐dimensional objective spaces for multi‐objective drug design.
Yifei Liu, Yiheng Zhu, Jike Wang, Renling Hu, Chao Shen, Wanglin Qu, Gaoang Wang, Qun Su, Yuchen Zhu, Yu Kang, Peichen Pan, Chang‐Yu Hsieh, Tingjun Hou   +12 more
wiley   +1 more source

Time Horizon Generalization in Reinforcement Learning: Generalizing Multiple Q-Tables in Q-Learning Agents

, 2009
Yasuyo Hatcho, Kiyohiko Hattori, Keiki Takadama   +2 more
openalex   +1 more source

Power Usage Reduction of Humanoid Standing Process Using Q-Learning [PDF]

, 2015
Ercan Elibol, Juan M. Calderón, Martín Llofriu, Carlos Quintero, Wilfrido Moreno, Alfredo Weitzenfeld   +5 more
openalex   +1 more source

State of the Art of Low‐Frequency Acoustic Modulation: Intensity Enhancement and Directional Control

Advanced Science, EarlyView.
High intensity low‐frequency sound sources hold significant value in many fields. However, their long wavelength, strong penetrability, and tendency to diffract make direction control and intensity enhancement challenging. Acoustic generators and metamaterial‐based acoustic devices still face issues such as low energy efficiency, poor directional ...
Jingsong Xu, Yunfeng Ye, Tao Dong, Zhaochu Yang, Nuno Miguel Matos Pires, Yu Zhou, Fuyu Tao, Jin Wang, Junshan Zhang, Guoxi Luo, Libo Zhao, Qi Mao, Yangtao Wang, Zhuangde Jiang   +13 more
wiley   +1 more source

Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shards. [PDF]

Heliyon
Sonavane SM, Prashantha GR, Nikam PD, A V R M, Chauhan J, S S, Bavirisetti DP.   +6 more
europepmc   +1 more source

Distributed interference management using Q-Learning in cognitive femtocell networks: New USRP-based implementation [PDF]

, 2015
Medhat Elsayed, Amr Mohamed
openalex   +1 more source

UniMap: Type‐Level Integration Enhances Biological Preservation and Interpretability in Single‐Cell Annotation

Advanced Science, EarlyView.
UniMap introduces a novel multiselective adversarial network that functions as a discerner in single‐cell dataset integration. It enables type‐level integration, ensuring accurate cell type annotation across diverse biological contexts while preserving shared and domain‐specific features.
Haitao Hu, Yue Guo, Fujing Ge, Hao Yin, Hao Zhang, Zhesheng Zhou, Fangjie Yan, Qing Ye, Jialu Wu, Ji Cao, Chang‐Yu Hsieh, Bo Yang   +11 more
wiley   +1 more source