Results 161 to 170 of about 137,924 (252)

Multi-Agent Reinforcement Learning in Games: Research and Applications. [PDF]

Biomimetics (Basel)
Li H, Yang P, Liu W, Yan S, Zhang X, Zhu D.   +5 more
europepmc   +1 more source

How AI Shapes the Future Landscape of Sustainable Building Design With Climate Change Challenges?

Advanced Science, EarlyView.
This review examines how artificial intelligence reshapes sustainable building design faced with climate change challenges. The authors synthesize existing studies to demonstrate AI's transformative potential across design lifecycle phases from climate‐aware form generation to performance optimization.
Pengyuan Shen, Xiaoni Gao, Shuai Lu, Yi Zhang, Xing Zheng, Matthaios Santamouris   +5 more
wiley   +1 more source

Attention-based multi-agent reinforcement learning for traffic flow stability in mountainous tunnel entrances. [PDF]

Sci Rep
Duan M.
europepmc   +1 more source

Adaptive Wireless Network Management with Multi-Agent Reinforcement Learning. [PDF]

Sensors (Basel), 2022
Ivoghlian A, Salcic Z, Wang KI.
europepmc   +1 more source

CXC Chemokine‐Driven Vascular Reprogramming: Modulating Tumor Vasculature to Boost Therapeutic Response

Advanced Science, EarlyView.
Tumor vascular remodeling is discussed from a chemokine‐centered perspective. This review summarizes the bidirectional, temporal, and tissue‐specific roles of CXC chemokines in regulating vascular function and immune accessibility. A functional vascular normalization score is introduced as a conceptual framework to integrate dynamic vascular and immune
Hongdan Chen, Yinde Huang, Juntong Wu, Ziying Yi, Yao Li, Zeyu Yang, Fan Zhang, Chong Li   +7 more
wiley   +1 more source

A Novel Data-Driven Multi-Agent Reinforcement Learning Approach for Voltage Control Under Weak Grid Support. [PDF]

Sensors (Basel)
Wu J, Wang Z, Han J, Li Q, Sun R, Li C, Cheng Y, Zhou B, Guo J, Long B.   +9 more
europepmc   +1 more source

Image Classification Method Based on Multi-Agent Reinforcement Learning for Defects Detection for Casting. [PDF]

Sensors (Basel), 2022
Liu C, Zhang Y, Mao S.
europepmc   +1 more source

Empowering Carbon Fibers With Ti3C2Tx MXene: A Paradigm Shift Toward Integrated Structure‐Function Composites

Advanced Science, EarlyView.
This review comprehensively outlines how Ti3C2Tx MXene transforms carbon fiber from a structural component into a multifunctional platform. We systematically detail cutting‐edge modification strategies and showcase exceptional performance in EMI shielding, energy storage, smart sensing, and beyond.
Hongshuo Cao, Yue Xing, Jiangman Sun, Yanhong Tian, Yangyang Gao, Xuejun Zhang, Xiubing Liang   +6 more
wiley   +1 more source

Maynard Smith revisited: A multi-agent reinforcement learning approach to the coevolution of signalling behaviour. [PDF]

PLoS Comput Biol
Macmillan-Scott O, Musolesi M.
europepmc   +1 more source

Scientific multi-agent reinforcement learning for wall-models of turbulent flows. [PDF]

Nat Commun, 2022
Bae HJ, Koumoutsakos P.
europepmc   +1 more source